Systems and methods for collecting, analyzing, and sharing bio-signal and non-bio-signal data

ABSTRACT

A computer network implemented system for improving the operation of one or more biofeedback computer systems is provided. The system includes an intelligent bio-signal processing system that is operable to: capture bio-signal data and in addition optionally non-bio-signal data; and analyze the bio-signal data and non-bio-signal data, if any, so as to: extract one or more features related to at least one individual interacting with the biofeedback computer system; classify the individual based on the features by establishing one or more brain wave interaction profiles for the individual for improving the interaction of the individual with the one or more biofeedback computer systems, and initiate the storage of the brain waive interaction profiles to a database; and access one or more machine learning components or processes for further improving the interaction of the individual with the one or more biofeedback computer systems by updating automatically the brain wave interaction profiles based on detecting one or more defined interactions between the individual and the one or more of the biofeedback computer systems. A number of additional system and computer implemented method features are also provided.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims all benefit, including priority, of U.S.Provisional Patent Application Ser. No. 61/701,002, filed Sep. 14, 2012,entitled REAL TIME SYSTEM AND METHODS FOR CLASSIFICATION OF MENTALSTATES USING EEG, and U.S. Provisional Patent Application Ser. No.61/701,176, filed Sep. 14, 2012, entitled SYSTEM AND METHODS FORCOLLECTING, ANALYZING, AND SHARING BIO-SIGNAL AND NON-BIO-SIGNAL DATA,the entire contents of each of which is incorporated herein by thisreference.

FIELD OF THE INVENTION

The present invention relates to bio-signal collection methods, andsystems that utilize bio-signal data.

BACKGROUND OF THE INVENTION

Bio-signals are signals that are generated by biological beings that canbe measured and monitored. Electroencephalographs, galvanometers, andelectrocardiographs are examples of devices that are used to measure andmonitor bio-signals generated by humans.

A human brain generates bio-signals such as electrical patterns known,which may be measured/monitored using an electroencephalogram (“EEG”).These electrical patterns, or brainwaves, are measurable by devices suchas and EEG. Typically, an EEG will measure brainwaves in an analog form.Then, these brainwaves may be analyzed either in their original analogform or in a digital form after an analog to digital conversion.

Measuring and analyzing bio-signals such as brainwave patterns can havea variety of practical applications. For example, brain computerinterfaces (“BCI”) have been developed that allow users to controldevices and computers using brainwave signals.

Training is often required before people are capable of performing tasksusing a BCI. This training generally requires the user to learn how toexert a measure of control over their brainwaves. However existingtraining methods for enabling users to learn sufficient control overtheir brainwaves to enable use of BC's are generally difficult to learn.

SUMMARY OF THE INVENTION

In one aspect of the invention, an improved training method is providedfor enabling users to learn how to control their brainwaves so as toenable the use of BCIs. A training method is provided that decreases thelearning curve for using a BCI. The system may identify characteristicsof a user's brain state to determine the user's cognitive or emotionalstate regardless of the user's ability to control a BCI.

In another aspect of the invention, an improved BCI system is provided.First, a novel and innovative BCI system is provided that obtains andanalyzes bio-signal data, and also systematically and selectivelyobtains non-bio-signal data, and analyzes the bio-signal data so as toimprove the analysis of bio-signal data. Certain relationships betweenbio-signal data and non-bio-signal were known, however, the inventor hasconceived of a system that may support a range of different applicationsthat relying on a BCI, and enables the collection of useful bio-signaldata and also non-bio-signal data in a way that supports real time ornear real time analysis of both data sets, so as to support for exampleaccurate and responsive performance of the applications that utilize theBCI.

Also, non-bio-signal data may not be considered when analyzingbio-signal data such as EEG data. However, environmental factors mayinfluence bio-signals. For example, ambient temperature may affect aperson's heart rate. Similarly, high stress situations may affect aperson's brain wave patterns.

Once, the data is collected for the user or the plurality of users, inaccordance with another aspect of the invention, a computer system isprovided that processes and analyzes the aggregated data. The aggregateddata may be shared (for example with other network-connected devicesthat are authorized to connect to the computer system of the presentinvention). In another aspect, the results of the analysis andprocessing of the aggregated data may be shared.

In another aspect, sharing may involve providing direct access to theindividual data or results or the aggregated data or results; or it maymean providing access to the data, results, or both through anapplication programming interface (API), or other known means.

Therefore, systems and methods are provided for collecting, analyzing,interpreting, and sharing bio-signal and non-bio-signal data. Aspectsand embodiments of these systems and methods may use analysisalgorithms, e.g. machine learning algorithms, to analyze the aggregateddata for the user or the plurality of users. It is predicted that thesystems and methods may be improved as the amount of aggregated dataincreases, whether it is more data for the user, more data for theplurality of users, or both.

Other aspects and embodiments of these systems and methods may collect,analyze and associate particular bio-signal and non-bio-signal data withspecific mental states for both individuals and groups. The collecteddata, analyzed data or functionality of the systems and methods may beshared with others, e.g. third party applications, other users, etc.

In an embodiment, one or more client devices are connected to internalsensors, external sensors, wearable sensors, and user effectors tocollect bio-signal and non-bio-signal data, and these devices are linkedto a server. A skilled person would understand the server may be local,remote, cloud based or software as a service platform (SAAS).Connections between the client device or devices, internal sensors,external sensors, user effectors, and the server may be encrypted. Thecollected data may be processed and analyzed on the client device ordevices, on the server, or both. Collected and analyzed data may be usedto build a user profile that is specific to a user. The user profiledata may be analyzed, e.g. by machine learning algorithms, eitherindividually or in the aggregate to function as a BCI, or to improve thealgorithms used in the analysis. The data, analyzed results, andfunctionality associated with the system can be shared with third partyapplications and other organizations through an API.

In another aspect, systems and methods for bio-signal and non-bio-signaldata collection and adaptive signal processing are provided. The systemsand methods may be used with mobile biofeedback applications.

In accordance with an aspect of the present invention, there is provideda system comprising: at least one client computing device; at least onebio-signal sensor at and in communication with the at least one clientcomputing device; and at least one computer server in communication withthe at least one computing device over a communications network; the atleast one client computing device configured to: receive time-codedbio-signal data of a user from the at least one bio-signal sensor;transmit the time-coded bio-signal data and acquired time-coded featureevent data to the at least one computer server; receive from the atleast one computer server a user-response classification of at leastpart of the time-coded bio-signal data based on an identified pattern ofcorrespondence between the at least part of the time-coded bio-signaldata and at least part of the time-coded feature event data at at leastone respective time code; in accordance with a received input confirmingthe user-response classification, update a bio-signal interactionprofile at the at least one client computing device with theuser-response classification, the at least part of the time-codedbio-signal data, and the at least part of the time-coded feature eventdata at the at least one respective time code.

A method, performed by at least one client computing device incommunication with at least one bio-signal sensor at the at least oneclient computing device, the at least one client computing device incommunication with at least one computer server over a communicationsnetwork, the method comprising: receiving time-coded bio-signal data ofa user from the at least one bio-signal sensor; transmitting thetime-coded bio-signal data and acquired time-coded feature event data tothe at least one computer server; receiving from the at least onecomputer server a user-response classification of at least part of thetime-coded bio-signal data based on an identified pattern ofcorrespondence between the at least part of the time-coded bio-signaldata and at least part of the time-coded feature event data at at leastone respective time code; in accordance with a received input confirmingthe user-response classification, updating a bio-signal interactionprofile at the at least one client computing device with theuser-response classification, the at least part of the time-codedbio-signal data, and the at least part of the time-coded feature eventdata at the at least one respective time code.

In this respect, before explaining at least one embodiment of theinvention in detail, it is to be understood that the invention is notlimited in its application to the details of construction and to thearrangements of the components set forth in the following description orillustrated in the drawings. The invention is capable of otherembodiments and of being practiced and carried out in various ways.Also, it is to be understood that the phraseology and terminologyemployed herein are for the purpose of description and should not beregarded as limiting.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments will now be described, by way of example only, withreference to the attached figures, wherein:

FIG. 1 illustrates an embodiment of the platform, including a possiblecloud service model, and a local trusted network of client devices;

FIG. 2 illustrates an embodiment of the platform that includes a cloudservice model for a user associated with a single client device;

FIG. 3 illustrates an embodiment of a cloud service model in accordancewith the invention, and further including integration with a third partyapplication integration;

FIG. 4 illustrates an embodiment of the architecture of a cloud serviceplatform in accordance with the present invention;

FIG. 5 illustrates an embodiment an analyzer using machine learningtechniques in accordance with the present invention;

FIG. 6 illustrates an embodiment of a use profile data structure inaccordance with the present invention;

FIG. 7 illustrates an embodiment of a sensor data packet in accordancewith the present invention;

FIG. 8 illustrates an embodiment of the platform in accordance with thepresent invention;

FIG. 9 illustrates an embodiment of a relaxation embodiment of enhancedmachine learning in accordance with the present invention;

FIG. 10 illustrates an embodiment of a method for discovering meaningfulpatterns in brainwave data in accordance with the present invention;

FIGS. 11A and 11B illustrate tables of data used during an example ofmeaningful patterns discovery in accordance with the present invention;

FIG. 12 illustrates a data mining improver and/or optimizer inaccordance with the present invention;

FIG. 13 illustrates an example of bin boundaries used for quantizationin accordance with the present invention;

FIG. 14 shows the Naïve Bayes formula used in accordance with an aspectof the invention;

FIG. 15 illustrates an example count and corresponding probability foruse in an example of the present invention;

FIG. 16 illustrates how the client device may be updated in accordancewith the present invention;

FIG. 17 illustrates a generic computer used to implement aspects of thepresent invention;

FIG. 18 illustrates an embodiment of the platform of the presentinvention; and

FIG. 19 illustrates an embodiment of a bio-signal state modulationcomponent of the embodiment of FIG. 18.

In the drawings, embodiments of the invention are illustrated by way ofexample. It is to be expressly understood that the description anddrawings are only for the purpose of illustration and as an aid tounderstanding, and are not intended as a definition of the limits of theinvention.

DETAILED DESCRIPTION

The present invention relates generally to computer systems and computerimplemented methods for collecting bio-signal and non-bio-signal data,applying data processing to the collected data, and using the collectedand processed data in biofeedback systems. More specifically, theinvention provides mechanisms for improving the performance ofbiofeedback systems based on a novel and innovative (A) computer systemand database architecture for collecting and processing bio-signal dataand non-bio-signal data, (B) specific computer system features andfunctions, and computer implemented techniques for utilizing bio-signaldata and non-bio-signal data to improve the performance of biofeedbacksystems.

As further explained below, the present invention provides a computersystem and a series of computer implemented methods that promote theadoption of biofeedback systems and improve their functionality forusers, in novel and innovative ways.

Individuals may differ from one another in the respective brainwavepatterns exhibited, and therefore the manner in which each individualinteracts with a BCI may also vary. Learning how best to interact with aBCI can be challenging and frustrating for individuals, and may affectadoption of biofeedback systems, or may cause users to begin using suchsystems, but not use them as much as they would if training on thesystems were improved. Discoveries in accordance with aspects of thepresent invention is that an individual may be characterized as havingparticular features or attributes to the way in which the individualinteracts with a BCI; that a feature extractor may be used to streamlinethe process of determining the parameters of how a particular individualmay interact with a BCI (and optionally classifying individuals based onthese features); and also that machine learning systems and methods maybe utilized in order to improve the efficiency of feature extraction andclassification. Further details and embodiments for implementing theseaspects of the invention are described below.

An embodiment of a system for bio-signal and non-bio-signal datacollection and analysis is provided comprising at least one clientdevice for collecting data, at least one computer server forcommunicating with the client device to process and analyze thecollected data together with the at least one client device, and sharethe collected and/or analyzed data with other client devices

Referring to FIG. 1, in an embodiment of the system there is shown atrusted network of local client devices 113 and a software as a service(SAAS) platform 200 operating at one or more computer servers. Thetrusted network of local devices may comprise, without limitation, oneor more of the following, in various combinations: a mobile phone,wearable computer, laptop, local computer, or trusted server.

As shown in FIG. 1, the trusted network of local client devices 113 andthe SAAS platform 200 may communicate via a network connection 301, forexample, a wired or wireless internet connection. Furthermore, thedevices in the trusted network may be configured to communicate witheach other. Systems and methods for local networking between devices areknown and non-limiting examples of such methods are wired, wireless,local peer-to-peer, and BLUETOOTH networking.

Referring to FIG. 2, an embodiment of the system is shown with a singleclient device 100 and a software as a service (SAAS) platform 200. Theclient device 100 may be, for example and without limitation, asmartphone, cell phone, or other computing device having one or moresensors. The client device 100 may communicate with the SAAS platform200 through a network connection 301, for example, a wired or wirelessinternet connection.

The start and endpoints of the above described connections 302 may beconfigured to encrypt and decrypt data transmissions using knownencryption techniques. Non-limiting examples of such techniques caninclude MD5 or SHA encoding. Additionally, protocols such as secure HTTP(HTTPS) can be used to encrypt the transmission medium.

As shown in FIGS. 1 and 2, internal sensors 106, external sensors 104,wearable sensors 102, and user effectors 110 may be operativelyconnected to the trusted network of local devices 113 (FIG. 1) or theclient device 108 (FIG. 2). A skilled person would understand that theinternal sensors 104, external sensors 106, wearable sensors 102 anduser effectors 110 may be operatively connected to one or more of thedevices in the trusted local network of devices or client device.Furthermore, a skilled person would understand that not all of theinternal sensors 106, external sensors 104, wearable sensors 102, anduser effectors 110 may be necessary to perform the methods describedherein.

Sensors for collecting bio-signal data include, for example,electroencephalogram sensors, galvanometer sensors, orelectrocardiograph sensors. For example, a wearable sensor 102 forcollecting biological data, such as a commercially available consumergrade EEG headset with one or more electrodes for collecting brainwavesfrom the user. A skilled person would understand that an EEG using fewerelectrodes would make the wearable sensor 102 more affordable toproduce, although an EEG with more electrodes would comparativelycollect more data, or collect data more accurately.

The one or more internal sensors 106, external sensors 104, or wearablesensors 102 may collect bio-signal or non-bio-signal data other than EEGdata. For example, bio-signal data may include heart rate or bloodpressure, while non-bio-signal data may include time GPS location,barometric pressure, acceleration forces, ambient light, sound, andother data. Bio-signal and non-bio-signal data can be captured by theinternal sensors 106, external sensors 104, or both.

The distinction between internal and external sensors refers to whetherthe sensor is contained within the client device (mobile computingdevice) 108. For example, a smart phone may have a plurality of built-ininternal sensors 106 for collecting non-bio-signal data. Externalsensors 104, such as wireless headsets or other types of sensors, maycomplement or replace the available internal sensors 106. The externalsensors 104 may be connected to the client device or devices of thetrusted local network 113 or mobile computing device 108 by wired orwireless connection, such as, for example, USB or BLUETOOTH connections.Other ways to connect external sensors to the client device or devicesin a trusted local network 113 or mobile computing devices 108 arepossible.

User effectors 110 are for providing feedback to the user. A “usereffector” may be many manner of device or mechanism for having an effecton a user or triggering a user, for example to act on a message. Forexample, user effector 110 could be a vibration, sound, visualindication on a display or some other way of having an effect on theuser. A user effector could be internal or external to the clientdevice, as was described above for internal sensors. Furthermore, theuser effector 110 may be connected to the client device in a mannersimilar to external sensors, as described above.

User effectors 110 can be used, for example, to provide real-timefeedback on characteristics related to the user's current mental statefor example. These user effectors may also be used to assist a user inachieving a particular mental state, such as, for example, a meditativestate. The user effector 110 may be implemented for example using agraphical user interface designed to enable a user to interact with ameditation training application in an effective manner.

In order to process and analyze collected bio-signal and non-bio-signaldata, methods and means for processing and analyzing the collected dataare required. Processing collected data can be performed by hardware,software, or some combination of the two. A skilled person wouldunderstand that the processing and analysis of data can be performed ona client device, a local server, a remotely located server, acloud-based server, a SAAS platform, or some combination thereof. Acloud-based platform implementation have provide one or more advantagesincluding: openness, flexibility, and extendibility; manageablecentrally; reliability; scalability; being optimized for computingresources; having an ability to aggregate information across a number ofusers; and ability to connect across a number of users and find matchingsub-groups of interest.

The processing and analyzing of collected data may be implemented incomputer software or hardware at the at least one client device and atleast one computer server. For simplicity, this combination of hardwareand software may be referred to as the processing and analyzing systemplatform (or “system platform”). Wherever reference is made to the term“cloud” or “cloud server”, this is intended to refer to the at least onecomputer server of the system platform. While embodiments andimplementations of the present invention may be discussed in particularnon-limiting examples with respect to use of the cloud to implementaspects of the system platform, a local server, a single remote server,a SAAS platform, or any other computing device may be used instead ofthe cloud.

Each time analysis or processing of user bio-signal data (such asbrainwave data) is performed, an instance of aspects of the systemplatform may be may be generated by the system platform, initiated ateither the client device or the cloud, in order to analyze the user'sprivate bio-signal data using particular analysis or processingparameters applied during the analysis or processing. For simplicity,such an instance may be referred to as the system platform “pipeline”.Each instance of the pipeline may have an associated pipeline identifier(“ID”). Each pipeline may be associated with a particular activity type,user, bio-signal type of a particular user, application, or any othersystem platform-related data. Each pipeline may maintain particularpipeline parameters determined to analyze the user's bio-signal data ina particular way, consistent either with previous analysis of theparticular user's bio-signal data, consistent with previous analysis ofone or more other user's bio-signal data, or consistent with updateddata at the cloud server derived from new or updated scientific researchpertaining to the analysis of bio-signal data. Pipelines and/or pipelineparameters may be saved for future use at the client computing device orat the cloud.

The system platform may preprocess the raw collected biological andnon-biological data 112. This can involve, as non-limiting examples,noise filtering and interpolating gaps in signals, and/or any othersolution for handling issues related to signal collection andtransmission.

The system platform may then use the preprocessed data to extractfeatures from the signal. For example, this may be done by projectingthe EEG signal into a lower dimensioned feature space. This projectionmay aid in the classification of the collected data, for example, byincreasing the separability of classes.

The system platform may then classify the extracted features of thedata. This classification may provide an indication of the mental stateof the user, such as a meditative state. Various algorithms 116 can beused to classify the features determined in previous stages. Forexample, a minimum mean distance classifier or a K nearest neighboursclassifier may be employed to analyze the data.

Feature extraction and classification may be performed by machinelearning systems of the system platform. In particular, supervisedmachine learning systems and methods may be employed to improve theaccuracy of the steps of feature extraction and classification.

Representative Embodiment of System—MED-CASP

In one implementation of the invention a Multi-modal EEG Data-Collectionand Adaptive Signal Processing System (MED-CASP System) for enablingsingle or multi-user mobile brainwave applications may be provided.

At the core of the application there is an implementation of the systemplatform, a cross platform mobile EEG platform specifically for enablingBCI applications. This system platform may be implemented is a hardwareand software solution that is comprised of an EEG headset, a client sideapplication and a cloud service component. The client side applicationmay be operating on a mobile or desktop computing device.

A particular system implementation may include a range of differentfeatures and functions, for example an EEG headset may be designed totarget the meditation (such as health and wellness, orhuman-performance) market segment, and may be designed to be usable withother BCI applications. Non-limiting features of this headset mayinclude: an unobtrusive soft-band headset that can be confidently wornin public; and differentiation from prior art consumer EEG solutionsthrough the use of 3, or 4, or more electrodes (rather than one). Thisadvancement may enable: estimation of hemispheric asymmetries and thusfacilitate measurements of emotional valence (e.g. positive vs. negativeemotions); and better signal-t-noise ratio (SNR) for global measurementsand thus improved access to high-beta and gamma bands, fast oscillationEEG signals, which may be particularly important for analyzing cognitivetasks such as memory, learning, and perception. It has also been foundthat gamma bands are an important neural correlate of mediationexpertise.

In the same or another non-limiting exemplary implementation, possibleMED-GASP system features may include: uploading brainwaves andassociated sensor and application state data to the cloud from mobileapplication; downloading brainwave & associated data from the cloud;real-time brain-state classification to enable BCI in games or otherapplications; transmitting real-time brain-state data to other userswhen playing a game to enable multi-user games; sharing brainwave datawith other users to enable asynchronous comparisons of results; sharingbrainwave data to other organizations or third party applications andsystems; and support of cloud based user profiles for storing personalinformation, settings and pipeline parameters that have been tuned tooptimize a specific user's experience. In this way, usage of the systemplatform can be device independent.

Each person's brainwaves are different, therefore requiring slightlydifferent tunings for each user. Each person's brain may also learn overtime, requiring the system platform to change algorithm parameters overtime in order to continue to analyze the person's brainwaves. Newparameters may be calculated based on collected data, and may form partof a user's dynamic profile (which may be called bio-signal interactionprofile). This profile may be stored in the cloud, allowing each user tomaintain a single profile across multiple computing devices. Otherfeatures of the same or another non-limiting exemplary implementationmay include: improving algorithms through machine learning applied tocollected data either on-board the client device or on the server;saving EEG data along with application state to allow a machine learningalgorithm to optimize the methods that transform the user's brainwavesinto usable control signals; sharing brainwave data with otherapplications on mobile device through a cloud services web interface;sharing brainwave data with other applications running on client devicesor other devices in the trusted network to provide for the user'sbrainwave data to control or effect other devices; integration of datafrom other devices and synchronization of events with brainwave data aidin context aware analysis as well as storage and future analysis;performing time locked stimulation and analysis to support stimulusentrainment event-related potential (“ERP”) analysis; and dataprioritization that maximizes the amount of useful informationobtainable from an incomplete data download (i.e. data is transmitted inorder of information salience). The core functionality of the MED-CASPsystem may be wrapped as an externally-usable library and API so thatanother developer may use the platform's features in the developer'sapplication(s). The library may be a static library and API for Unity3D,iOS, Android, OSX, Windows, or any other operating system platform. Thesystem platform may also be configured to use a pre-compiled algorithmsupplied by a third party within the library, including the ability fora third party developer using the library, to use the developer's ownalgorithms with the library. The system platform may also supportheadsets from a variety of vendors; personal data security throughencryption; and sharing of un-curated data (optionally usingtime-limited and fidelity limited access) though the sharing ofencryption keys.

Various implementations of the system platform are possible. Inparticular, various different applications may be developed that utilizethe system platform and enable different types of biofeedback systems.Non-limiting examples of such applications are provided herein.

Mobile Device Based Application—Processing at Mobile Device

A data flow for a meditation session with one user in accordance with anexemplary, non-limiting implementation of the present invention isdescribed. A user may execute an application (or “app”) on the mobiledevice (or client computing device). The application may update adisplay on the mobile device to prompt for input of a user identifier(“ID”) and password. Upon receiving the user ID and password, thisinformation may be sent to the cloud server together with an applicationID associated with the application executing on the user's mobiledevice, for processing by security software or hardware (the “securityengine”) at the cloud server. If the user ID is associated with thepassword then the security engine sends a message back to the mobiledevice to display or provide access to at least a part of the user'suser profile. The message may further include data for display at themobile device, including all or a subset of the data stored in theuser's profile at or accessible to the cloud server. If the cloud serverhas computed any new or updated pipeline parameters per activity typesince a prior login, these parameters may be sent to the application onthe mobile device. The new or updated pipeline parameters may be basedon previous calibration exercises and/or data mining and/or newscientific evidence resulting in a change in how the cloud serverassociates brain wave data with particular parameters. The cloud servermay further transmit status of the user's online friends, messages fromfriends, messages from cloud administrators, or other data to theapplication on the mobile device.

The mobile device determines if an EEG headband is connected to themobile device. If no EEG headband is found, instructions on how toconnect the headband are displayed on the mobile device display.

Upon connecting an EEG headband with the mobile device, for example viabluetooth or other near field communication protocols, the EEG headbandstreams EEG data to the mobile device. The mobile device transmits theEEG data to the cloud when the EEG data is received at the mobiledevice. If the mobile device or the cloud server detects that the EEGheadband is not presently being worn by the user, then the user may beeither prompted to wear the EEG headband, or the user may be prompted toinput other information including, for example, the user's intended useof the device. Some applications at the mobile device may opt to notsend data to the cloud when the headset is not being worn.

The user may select a specific activity type (e.g. meditation exercise)from a screen displayed by the application. The app assigns a session IDwhich may be uniquely derived based on calendar data and time.

Parameters of a meditation algorithm used in the meditation exercise maybe calibrated in a variety of ways. In one non-limiting example, theuser is guided through a busy mind exercise such as word association oran exercise designed to evoke self-referential thinking. Next, the usermay be asked to quiet the user's mind and focus on the user's breath orto complete a body scan. The app may receive a pipeline ID plusassociated parameters sent by the cloud when the app is firstinitialized to process the EEG data. The meditation app may then beready to use by the user. The mobile device may transmit the followingdata or information to the cloud server: app ID associated with thisapplication; session ID; user ID; activity type (e.g. calibration-busymind); and app data including signal type (e.g. EEG), timestamps per EEGsample, timestamps associated with start and stop of activity, and rawEEG data per channel. Other additional sensor data may be sentstructured same way as EEG signal type.

The app may apply the pipeline parameters to process the EEG data in thedevice and it is used to provide feedback to the user through visualscreen graphics, audio or tactile feedback. The start and stop timestamps of a session along with the EEG is sent to the User's Profileswith the APP ID, and session ID of the Activity Type. Time stampsassociated with the start and end times or significant events such asevent related potentials are send to the Cloud. These timestamps areused by the Cloud delineate the different parts of an Activity Type andare stored in the user's profile in the cloud where each time stampbeginning and end is labelled with the type of event it is associatedwith (e.g. start of Activity, End of Activity, Event Related Potentialstimulus, etc.).

Any signal processed data such as EEG band power calculated by the APPis sent to the Cloud per Activity Type. The pipeline in the app uses theprediction model defined by that pipeline ID to calculate a score orfeedback. The user receives feedback for each part of the user's sessionthat is computed from the user's EEG signal. These computed results canbe either aggregated across parts of the entire session or across theentire session. These results are stored under a session ID in the userprofile associated with the app ID. If the cloud altered the user'sparameters for a specific pipeline ID then they are stored in the user'sprofile in the cloud and are transmitted to the app upon next start-upof the app. Computed results can also have time stamps associated withthem and the name of the parameter being computed (e.g. score is 192 attime 1:10). Results presented to the user are stored in the user'sprofile associated with the app ID, activity type, and session ID. Theresults may be filtered by the system platform to retain salientelements of an activity. The feedback may also include instructions sentto the user from the cloud to the device so that the user may be guidedto improve the user's subsequent sessions. This feedback can be kept upto date in the cloud as bio-signal analysis evidence grows and changesfrom literature and or from patterns observed of the user.

Optionally, EEG data not related to a specific app activity type may betransmitted to the cloud by the application on the mobile device. It maybe valuable to transmit EEG data to the cloud even if a user is notengaged in a specific exercise. If an EEG signal is being received bythe device from a headset and if the EEG signal quality exceeds aminimum threshold while being worn by the user and if the user's privacysettings allow then EEG data streams are transmitted to the cloud usingan activity type such as “No Prescribed Activity”.

The app may provide for switching between available pipelines byreceiving instructions from the cloud identifying a pipeline ID andassociated pipeline parameters. The cloud server may analyze eachactivity type and determine which pipeline to choose for this user. Thepipeline chosen may also depend on other information gathered fromuser's profile such as age and or as a direct response to surveyquestions or if a user suffers from a particular disorder. Otherinformation that is used to fine-tune the algorithm pipeline is ahistory of the sessions of the user, including number of sessions, totalnumber of session hours, time of day of session, session scores andresults. Machine learning may be used to determine patterns across auser's sessions to change and adapt a particular user's pipeline. Thecloud server transmits the pipeline ID to the device along with thelearned parameters. In addition, the specific parameters of the pipelinecan be determined within the device if disconnected from the cloud. Ifthis is a first time user of the app then a default pipeline may bechosen for the user. The parameters for the pipeline or pipeline ID plusparameters may be sent to the user device. Again, the default pipelineparameters may be fine-tuned based on demographics or survey responses.The user can be part of an evaluation to select among a number ofdifferent algorithm pipelines. When a new pipeline is created for theuser, the cloud may provide a new pipeline ID to be associated with thenew pipeline at the cloud and at the client computing device.

Mobile Device Based Application—Processing at Cloud Server

Optionally, instead of the app applying the pipeline, the cloud mayperform the pipeline processing. This may be achieved by selecting anoption in the app for cloud-based pipeline processing. The cloud maystore all of the information received from the mobile device in theuser's profile in the cloud. The cloud may select the pipeline ID inaccordance with the particular application. The cloud may maintain anassociation of one or more pipeline ID with particular applications orapplication types. In a typical pipeline the cloud server may applyIndependent Components Analysis (“ICA”) on the EEG signals to removeunwanted artifacts from the EEG signals. ICA may also be used toidentify the source locations of EEG signals in the brain. ICA iscomputer resource-intensive and the cloud has significantly morecomputing power than a mobile device. The cloud calculates theparameters for this pipeline from the user profile that have beencustomized for this user and that have been adapted during anycalibration of analysis of the user's particular bio-signal data. Anysignal-processed data such as EEG band power calculated by the cloud isstored in the cloud per activity type. This information can also be sentto the mobile device app for storage in the mobile device user profile.The cloud may calculate feedback in real time using the pipeline definedby that pipeline ID. The cloud transmits feedback to the mobile deviceapp. The feedback can also include instructions sent to the user fromthe cloud to the device so the user can be guided to improve asubsequent session.

The mobile app provides feedback to the user through visual screengraphics, audio or tactile feedback. The user receives feedback for eachpart of the user's session that is computed from the user's EEG signal.These computed results can be either aggregated across parts or acrossthe entire session. These results are stored under a session ID in theuser profile associated with the app ID. If the cloud altered the user'sparameters for a specific pipeline then the parameters are stored in thelocal user profile at the mobile device. Computed results can also havetime stamps (or time codes) associated with them and the name of theparameter being computed (e.g. score is 192 at time 1:10). Resultspresented to the user are stored in the local user profile associatedwith the app ID, activity type, and session ID. The results may befiltered down to retain salient elements of an activity. The feedbackcan also include instructions sent to the user from the cloud to thedevice so the user can be guided to improve their subsequent sessions.This feedback can be kept up to date in the cloud as evidence grows andchanges from literature and or from patterns observed of the user.

User Profile Data Structure

User profile data 114 may comprise, without limitation, raw collecteddata from the sensors, processed data, analyzed data, bio-signal dataand non-bio-signal data, transformation matrices, algorithm parameters,and the data may be associated with time, and passkey or authenticationdata required to access the data or establish a connection between theclient device and the SAAS platform 200.

An example of a possible user profile data structure is shown in FIG. 6.The user profile data 114 may be described in a relational databasestructure, and may include the following data: user id; demographicdata; security data; and preference data. The user profile data 114 mayfurther link to or comprise any of the following data or datastructures: user session data (including user id; session id); sessiondata (including session id; raw data such as signal type and channel;computed results; timestamps (such as begin/end; and event); duration;application id; app sequence number; algorithm pipeline parameters;activity type); user algorithm pipeline settings (including user id;algorithm pipeline id; demographics; security; preferences); algorithmpipeline data (including algorithm pipeline id; version; defaultparameters); user app globals (including user id; application id;aggregated results; application settings); application data (includingapplication id; version; app sequence number; settings); group members(including user id; group id); and group data (including group id; name;description; preferences).

The User ID may be a unique number identifier that uniquely identifies aUser Account. The demographics data may include gender, age, education,country, city, or other demographic data. The security data may includepassword and security questions. The preference data may includepreferences for the display and User Interface, and other aspects of theapplication or system. The user profile data 114 may also includesharing preference data describing the policies of sharing data withothers, and may be keyed to a separate table.

The User session data may include the user id and a session id uniquelyidentifying one session with an application.

The session data for each session id may be stored in a separate datastructure. The raw data for a particular session may be a time-based(time-coded) series of biological data (bio-signal data) from one ormore sensor types and the corresponding channels (e.g. EEG channel 2).The timestamps may be a time stamp (or time code) for each sample in therespective series for that session. The session data may also includetime stamps of activity within a session describing the start and endtime of an activity type within a session. The computed results mayinclude scores or other results computed from the session data. Theapplication ID may comprise a number that identifies a uniqueapplication (i.e. software program) that was used to create thissession. The app sequence number may represent the sequence number thatthis app was used in a running total of session across all usersmaintained by the application. The pipeline parameters may include apipeline ID which identifies the steps needed to process each differenttype of biological signal captured in the raw data. The activity typemay describe each type of activity associated with the session. Forexample there may be a Calibration Activity Type within a Brain FitnessActivity Type.

The pipeline ID may include a user pipeline setting 1D of the algorithmpipeline settings may include a key to an entry in this table thatdefines an algorithm pipeline and its parameters to be used perapplication per user. The algorithm pipeline ID may further include analgorithm pipeline ID including an index or key to the algorithmpipeline table. The algorithm pipeline settings preferences may includealgorithm pipeline parameters such that for each algorithm pipeline, aset of parameters may be used to specify the properties of each step inthe pipeline (i.e. use a bandpass filter between 3 to 30 Hz).

The algorithm pipeline may include a name field including descriptivetext of what the algorithm pipeline accomplishes, and may furtherinclude a steps field including descriptive text of the steps and theirorder applied of the pipeline. The version field may include a differentnumber for each iteration of the pipeline. This version number can indexold algorithm pipelines. The default parameters may define parametersfor the algorithm pipeline when a person first starts using thealgorithm pipeline.

The user app globals data may store any aggregated results or statisticsacross all sessions. The aggregated results may include total scores,badges, averages, total number of sessions, and other aggregatedresults. The application settings may include user-specific preferencesfor this application such as turn sound off, colour of display, andwhether to store session data on the server. The set of algorithmpipelines may be customized per user per application.

The application data name may describe the application, and version maydescribe the version number of the application. The app sequence numbermay provide a total number of sessions run by this App for all users,and the settings may include global settings of the application for allusers.

The group members may include a user ID which specifies a single user inthe group specified by the group ID, and the group ID identifies a groupof users.

The group data is identified by the group ID. The group data'srespective name field provides text describing the group. Thedescription field provides text describing the purpose and other detailsof the group. The preferences field may include whether the group isopen or closed for other to join, and other preferences.

Encoding Methods

It may be beneficial to ensure time-locked fusion across sensors used inaspects of the present invention. Time stamps may be used for thispurpose. Time stamps in the cloud could yield a reduction in compressionor dimensionality by considering the joint space of many sensors. Forexample, encoding may be performed as a covariance matrix of a multitudeof sensors and features. Time stamps may be assigned by the mobiledevice to brainwave data or any other measured or received data, and maybe transmitted to the cloud server.

In the cloud algorithm pipeline, every stage may dump data into a highperformance time indexed database Hypertable. Hypertable is an opensource database system inspired by publications on the design ofGoogle's BigTable. Data may be cached in random access memory (“RAM”)(such as ring buffers) of the system platform or flash as arrays beforebeing put into a database such that database read/write can bedecoupled. This may allow each algorithm to grab the most recent and allthe necessary history in order to work.

Data should be marked with the timestamp of when it happened as well asa timestamp of when it appeared or when it was ready. This allows forthe creation and use of latency measures that are data dependent, andmay be used to synchronize, calibrate, or relate clocks at the mobiledevice with clocks in the Cloud. As later information is calculated thiscan be inserted (for example, by time of occurrence) and be availablefor other processing.

A time stamp may also be used with a labeled epoch. The purpose of theepoch is to give a well-defined label to a section of data withsufficient length such that features of the epoch are discriminative.This may allow for efficient querying and retrieval of information. Analternative to epoch, may be a time series, with event markers in thedata stream that assign labels to a range of times. Databases such asHypertable may be used for querying time ranges and extracting recordsthat can have label time span information stored within. Optionally,bracketing the windows may assist in finding the markers, in particularwhere a greater than zero time span is employed.

In a non-limiting example, row keys in a Hypertable may be sensor name,date, and time. To fetch data from a set of sensors for a range of timemay be done as follows with the following query: SELECT*FROM SensorDataWHERE (“sensorMUSE 2012-07-29 12:29:00”<=ROW<“sensorMUSE 2012-07-3012:29:00” OR “sensorECG 2012-07-29 12:29:00”<=ROW<“sensorECG 2012-07-3012:29:00”).

Example Encoding of Brainwave Signals

In an exemplary, non-limiting implementation of the present invention,the mobile device may receive raw EEG data from the brainwave sensor(for example, the EEG headband) in real-time. Mobile devices, such asiOS-based devices may exhibit data bandwidth limitations. Some mobiledevices may be limited to receive bluetooth data at 6 Kbit/s per secondto 10 Kbit/s. A golem coding compressing method may be employed toimprove the reliability of such data transmissions to the mobile device,and to achieve 256 Hz sampling rates. Ideally a slightly higher samplingrate would be desired as artifacts such as electromyography (“EMG”)muscle noise generates significant power up to about 150 Hz (which wouldmean that a sampling rate of greater than 300 Hz would be needed tofully capture the signal). With 256 Hz sampling rate, the effective passband goes up to about 120 Hz, which allows the presence of EMG signalsto be discriminated within the EEG. Headsets that utilize a low samplingrate such the emotiv epoc (125 Hz) cause the EMG artifacts to masqueradeas EEG, due to the limited passband, and make it hard to detect whetherwhat is being measured is actually EEG or artifact.

A concern of having a reduced sampling rate is that the possibility ofachieving a spike filtering method based on the specific patternproduced by motor unit activations in the frontalis muscles would becomeimpossible, however at 256 Hz, the EMG spike profile still appears to bewell-defined in a raw EEG time-series. With 4 channels of sampling at256 Hz, artifact filtering methods such as spatial filtering determinedthrough ICA or subspace reconstruction methods, are able to yield muchbetter results than frequency based filtering, which is all that iscurrently available for single channel data.

The sampling rate is limited by the maximum bandwidth of the bluetoothchannel. Should the bluetooth bandwidth of mobile devices increase, thenit may be possible to achieve similar sampling performance to what isachieved on a desktop personal computer (“PC”). On a PC much highersampling rates are possible, such as, for example a sampling rate ofapproximately 500 Hz. The higher sampling rates allow for improvedperformance.

Ten bits of precision per sample may be used to transmit a high qualityEEG signal. The measurement noise floor may be designed to be equivalentto the least significant bit of the 10 bit Analog to Digital Conversion.The iOS platform has sufficient bandwidth at 256 Hz to transmit at 10bits per sample ensuring high quality EEG signals.

The iOS compression method also reduces the precision of large signalswings, however, these are only produced when there is a problem withhow the headset is being worn (such as being off the head, or havingindividual electrodes making poor contact). These signals may beprimarily used to indicate to the user that the headset is producingpoor EEG measurements. In testing, no loss in performance is createdfrom this loss in fidelity.

Another effect of the low iOS bluetooth bandwidth is that theaccelerometer data must be sent out at much lower rates than with PCuse. This may limit some future methods that try to characterize complexmovement of the head at the same time as brain-state classification.However for basic gestures, such as tapping, the accelerometer hasonboard processing that eliminates the need for streaming the inertialdata, and the data needed for cognitive training related measures suchas posture tracking or breath/body entrainment, fits, along withsimultaneous EEG, within the iOS bandwidth limitations.

EEG Headset Data Compression Technique

The compression will be broken down into two steps. In the first step, adifferencing method may be used to reduce the numerical size of thedata. The differencing method may comprise an initial data value in thepacket being sent without compression. The subsequent value may then bedifferent from its immediate predecessor. For example, dataset [20, 18,11, 16, 16, 10, 4, 15] translates to [20, 2, 7, −5, 0, 6, 6, −11].

In the second step, the difference data is then sent using a specialdynamic size. A median for the differences will be found and then thedifference data will be broken into three chunks. The chunks will be thequotient, remainder and sign of each difference point.

The first chunk will be the quotient of a division from the absolutemedian converted to unary code. Using the above example, the absolutemedian difference is 6. For the purposes of the compression alldifferences will be taken its positive form. For example, [2, 7, −5, 0,6, 6, −11] will have quotients of [0, 1, 0, 0, 1, 1, 1].

Unary Code is a simple bit representation of the number with 1'sindicating it's size. Unary Code of 5 is 111110, 1 is 10, 0 is 0.

The second chunk is the remainder from the division of the difference bythe median. This is also a dynamically sized value using TruncatedBinary Encoding. Again using the sample above we have another set ofdata with varying remainders. For example, [2, 8, −6, 0, 6, 6, −11] willhave remainders of [2, 1, 5, 0, 0, 0, 5].

Truncated Binary Encoding allows some remainders to be expressed with 1less bit than the larger remainder values. Using an example of a medianof 6, following ceiling of log 2(6) we know a bit number of 3 isrequired to represent 0-5. 3 bits have 8 states, therefore 8-6 is 2 freeunused states in 3 bits. This means we can represent the number 0 and 1with 1 less bit than 2, 3, 4 and 5. We do this by converting 0 and 1 tosimply 00 and 01 respectively. However bits 2, 3, 4 and 5 are translatedup by the free state amount of 2. Therefore, 2 is 4, which is 100 and soon. For example, 0:00; 1:01; 2:100; 3:101; 4:110; and 5:111. Note thatstarting with 1 indicates that the value is not 0 or 1 ensuring all 3bits included.

To indicate how this might work for various other examples of 11, 23 and9 follow. For the 11 example: 0:000; 1:001; 2:010; 3:011; 4:100; 5:1010;6:1011; 7:1100; 8:1101; 9:1110; and 10:1111.

For the 9 example: 0:000; 1:001; 2:010; 3:011; 4:100; 5:101; 6:110;7:1110; and 8:1111.

For the 23 example: 0:0000; 1:0001; 2:0010; 3:0011; 4:0100; 5:0101;6:0110; 7:0111; 8:1000; 9:10010; 10:10011; 11:10100; 12:10101; 13:10110;14:10111; 15:11000; 16:11101; 17:11110; 18:11011; 19:11100; 20:11101;21:11110; and 22:11111.

As shown the values of reduced size vary based on the individual median.The median is chosen over the mean to implement the maximum numberresults expressed as 0 in Truncated binary as well as only 1 in Unary,in the above 3 examples the value would be 10000, 100000 and 10000respectively. The final chunk is simply the value sign. If thedifference is negative the bit is 0 and if it is positive the bit is 1.The entire sample will then be a Unary Code, followed by a TruncatingBinary Code concluded with a sign bit. Each data set can vary in size asnecessary for transmission as well as compression.

As a countermeasure to large quotients from unexpected outliers a simpledynamic encoding called Elias encoding is used. A Unary of 15 indicatesa value larger than 14 quotient. This is followed by Elias encoding ofthe number, which is done through 0's leading to the binary numberindicating it's size (i.e. 5 in Elias would be 00101, 85 would be0000001010101).

The whole sample of 85 would be 1111111111111110000001010101 instead of85 1's.

The packet design structure may include a sync byte (0xFF); counter andtype (1 byte); 1 byte per median (4 bytes); length (2 bytes); checksum(1 byte); and sample difference data, 6 samples at a time, each channelafter the previous (size varies). In general, each packet may include a9 byte header followed by a variable size of the data set of 24 samples,6 samples from 4 channels. The Median may allow the other side tointerpret the results.

As a final measure of data transmission size a method is used to avoidsending data with large variances in the signal. If the median of thedifferences between the data neighbours exceeds 30, all differences aresent as a 0 difference. This makes the transmitted signal a set of 0differences resulting in a flat line where bad data is present.Optionally, a full absolute packet may be sent following the first gooddata packet to realign the absolute values for the individual channels.

The sensor device (EEG headband) may use a standard Bluetooth RFCOMM(serial port protocol) channel to transmit the raw EEG signals. The datamay be continuously transmitted as soon as a Bluetooth connection isestablished with a host. The system outputs data at a sample rate of125, 250 or 500 Hz. Each sample may contain 52 byte worth of data thatincludes a header, 16 EEG channels and a tail. FIG. 7 shows arepresentation of a sample packet structure as transmitted from thesensor device.

The packet starts with the byte ‘0xFF’ for synchronization. No otherbyte in the packet can have the value 0xFF by design. The second byte isa counter that increments from 0 to 0x8F.

Each EEG channel is a 24-bit sample that is broken up into 3 8-bytechunks with the most significant bit (“MSB”) first, the middle leastsignificant bits (“LSB”s) and the smallest LSBs. The 24-bit 2'scomplement value can be reconstructed as follows:

The 24-bit sample can be converted to raw EEG (in volts) by:EEG=sample/2̂24.

The second to last byte is a 7-bit value representing the battery level.

The final byte indicates the current sampling rate: 0-500 Hz, 1-250 Hzand 2-125 Hz.

Definition of System Platform Pipeline

The processing, analysis, and classification of brainwave data may beperformed in real-time. That is, data may be processed, analyzed, andcategorized as it is received from the sensors. Optionally, the resultsof the analysis may be used to provide feedback to the user through theuser effector 110. For example, analyzed data from one or more sensorsmight be analyzed to determine that a user is outside of a desiredmental state, and the system platform can use the user effector toassist the user in achieving the desired mental state. The processing,analysis and categorization of sensor signals is done by a systemplatform pipeline.

The data may be stored or otherwise associated with a user profile 114.This user profile 114 may be stored, for example, on one or more clientdevices in the trusted network 113, on a single client device 108, on alocal server, on a cloud-based server, on a SAAS platform 200, or somecombination thereof. Multiple user profiles may be stored in arespective data store, regardless of whether the data store is on aclient device, a local server, a cloud-based server, a SAAS platform 200or some combination thereof.

The pipeline may exist in the mobile device and in the cloud. At themobile device, the pipeline may be tuned for the mobile device'scomputational resources and application needs. The sensor hardware mayperform sampling; time stamping; and encoding of data for wirelesstransmission including pre-filtering (such as anti-aliasing, noiseshaping, etc.) and compression methodologies, to minimize theinformation loss at different levels of bluetooth bandwidth. The mobiledevice, which is the recipient of sensor data, may perform noiseidentification (such as spatial filtering and classification todetermine ideal denoising practices); denoising (such as linear subspacemethods, wavelets); spatial filtering, determined using techniques suchprincipal components analysis, blind source separation (such as ICA),maximum noise fraction (“MNF”), common spatial patterns; band passfiltering; and adaptive algorithms in the time domain or feature domain(such as adaptive whitening).

At the cloud, more advanced feature methods may be performed that may betoo computationally intensive or complex for performance on a mobiledevice. For example, empirical mode decomposition (“EMD”), ensemble modedecomposition (“EEMD”), or scattering wavelet transform (see StephaneMallat, “Group Invariant Scattering”, 15 Apr. 2012, web:http://arxiv.org/pdf/1101.2286.pdf, the entirety of which isincorporated by reference). These advanced methods can yield highlydiscriminative features that can yield much better machine learningperformance through greater separability, and reduced search spacesthrough filtering or built in invariances (such as in the scatteringnetwork transform). Resulting discovered patterns can then be discoveredin the data using simpler methods once we know what we are looking for.A secondary process may be included in the pipeline to transition fromdiscovered patterns to more computationally efficient but adequatelyperforming alternative analytical methods.

A pipeline in accordance with the present invention may specify thecomponents and the order in which a biological signal is processed,analyzed and interpreted. There are four major categories of componentsin the pipeline: signal processing, feature extraction, featureselection and prediction models.

Client side processing of the data may be tuned to conserve on computeoperations due to the battery and other resource limitations of mobiledevices. Offline cloud methods may run many different pipelines andselect a highest performing configuration (in terms of classificationperformance) and provide pipeline parameters to the client. Pipelineselection utilizes feature selection methods such as stepwise regressionor mRMR, or wrapper methods that estimate a classifier based on selectedfeatures are part of the feature selection methods.

Determination of correct data labelling may be a critical part ofestimating the optimal pipeline. The cloud methods are constructed toutilize map-reduce to parallelize estimators and estimate the mostlikely result based on all methods. Best estimates of class labels arefed back through system to denoise data (such as trim outliers, etc.,for methods that are prone). MapReduce operations may be used todistribute signal across feature methods, and features across classifiermethods.

Constraints relating to the real-time computational resources areintroduced into the feature selection process to ensure that theresulting real-time estimators are runnable on the target platform.Parameters will be platform dependant. For example, an applicationrunning on an iphone3 will have a different constraints than and iphone4which will have different characteristics than a desktop computer.

Pre-Processing or Signal Processing

The system platform may preprocess the raw collected biological andnon-biological data 112. This can involve, as non-limiting examples,interpolating gaps in signals, noise identification, noise filtering,spatial filtering, band-pass filtering. Other solutions may be employedfor handling issues related to signal collection and transmission.

Signal processing is a broad area that refers to mathematical methods ofextracting or transforming signals. The goal is to pre-process thesignal to remove unwanted noise and artifacts or to focus on a narrowfrequency bands of interest.

Signal processing may include: filters (frequency responsecharacteristics) (may include: low pass; high pass; and bandpass); orderof filters; artifact detection method (common spatial patterns,Threshold (e.g. log bandpower, signal power), high frequency EMG withspatial patterns, off); artifact removal method (ICA, repair bursts,ignore sections with artifacts); resample rate; EEG channels to use;electrode referencing scheme (average reference, specific electrode,linked mastoids); data cleaning (remove flatlines, remove brokenchannels, remove spikes or bursts); baseline removal (off or on); andwindow definition (size and increment).

Feature Extraction

The pipeline may use the preprocessed data to extract features from thesignal. For example, this may be done be done through linear projection(such as Short-time Fourier transform, “STFT”) of the EEG signal ornon-linear functions (such as fractal dimension) of the EEG data. Theresulting feature space may have greater or lesser dimensions than theoriginal data. This projection may aid in the classification of thecollected data, for example, by increasing the separability of classes.

Examples feature methods used by the system (operating on originalsignal or features of the signal) may include: short time fouriertransform; wavelet; AR model coefficients (auto regressive model of thesignal); Non-linear complexity features such as fractal dimensions(Hilbert transform); ensemble empirical mode decomposition; signalderivatives; and regularized covariance matrix estimation (such asledoit wolfe estimator).

Feature extraction is also a form of signal processing, however the goalmay be to extract features that are useful for machine learning to buildprediction models.

Frequency domain signal processing may include: fast fourier transform(“FFT”), wavelet or Hilbert; kernel definition (Hamming window, wavelettype); power spectrum; power per EEG band; power variance per EEG band;peak frequency within a particular band; and phase differences acrosselectrodes within EEG frequency bands.

Time domain signal processing may include: window sub-segments; voltageor signal power thresholds; and quantization or discretizationthresholds to encode continuous feature value to discrete value.

Transforming data to extract features may be accomplished at least byusing principal components analysis, linear discriminant analysis, eigendecomposition, and/or whitening transformations.

Feature Selection (Feature Adaptation)

An observed problem with BCI technology is that methods that are derivedfrom training data tend to be inconsistent in their performance andoften get worse as the user uses them. A primary case of this is thecovariant shift in the features with respect to the user responseclasses. Three main strategies are used in the system to reduce thiseffect: (1) server side algorithm improvement methods are run to updatethe client's algorithm pipeline and parameters per-session or as needed;(2) online covariance matrix estimation and subsequent whitening usingthis estimated covariance method (covariance matrix estimation isregularized to improve on stability); and (3) online detrending offeatures using fitted models such as low dimensional polynomial.

Prediction Methods

Parameters for prediction methods may be developed by machine learningin the client or in the server. Machine learning done in the cloud cansend parameters to the prediction model in the client. Parametersdiscovered in the client can be sent to the cloud to be stored in theuser profile there. The pipeline may then classify the extractedfeatures of the data. This classification may provide an indication ofthe mental state of the user (e.g. a meditative state). Variousalgorithms 116 can be used to classify the features determined inprevious stages. For example, a minimum mean distance classifier or a Knearest neighbours classifier may be employed to analyze the data.

Prediction methods may provide for: estimation of hemisphericasymmetries and thus facilitate measurements of emotional valence(positive vs. negative emotions); better SNR for global measurements andthus better access to high-beta and gamma bands (fast oscillation EEGsignals), which is particularly important for analyzing cognitive taskssuch as memory, learning, and perception (it has also been found thegamma is an important neural correlate of mediation expertise); noisereduction; integration of data from other devices and synchronization ofevents with brainwave data to aid in context aware analysis as well asstorage and future analysis; time locked stimulation and analysis tosupport stimulus entrainment ERP analysis; and data prioritizer thatmaximizes the amount of useful information obtainable with an incompletedata download (i.e. data is transmitted in order of informationsalience).

These prediction models are developed using machine learning methods, ofwhich learning types may include: linear discriminant analysis; supportvector machine; decision tree; K nearest neighbour; pattern discovery;Bayesian networks; and artificial neural networks.

Client-Side Rules Engine

The client computing device may implement part of the pipeline through aclient-side rules engine, which may be provided in computer hardware orcomputer software. The rules engine may: choose configurations of thepipeline; manage adaptation of particular elements (e.g. feature bands,reward levels, etc.); manage the user profile; manage or provide contextswitching; manage or provide context estimation; determine what data isto be transmitted or saved; determine which devices or users anybio-signal or non-bio-signal data may be provided to; determine whatresults are presented to the user; control the data that is provided foranalysis by the pipeline; decide on context based on a classifier (suchas a decision tree where context and confidence in class is used todecide which rules to use); control what sensors to use; determine alearning goal; and determine what pipeline to use. The rules engine maybe configured to know what sensors or other resources are available, acontext estimate, and application needs.

A client-side rules engine 26 may be provided in the mobile application,as shown in FIG. 8 in accordance with a non-limiting exemplaryimplementation of the present invention. Rules are loaded to the appfrom the cloud (server computer 10). The rules engine 26 may choosewhich pipeline that the analyzer 30 will apply to which sensors 18 of aspecific user. The rules engine 26 may provide rules 28 that use thecontext of the user (i.e. emotional state, global positioning system(“GPS”) coordinates, time of day, open apps on the device, prior goalsthat the user has stated) to determine how to analyze the sensor data(i.e. which algorithm pipeline to apply to which sensor). The rulesengine 26 may also have rules 28 to determine which results aredisplayed and or stored locally on the client 14 and which are stored inthe server 10.

Rules engine inputs may include: local user profile; and local appinformation (such as status (ON or OFF) identifier, parameters). Rulesengine outputs may include: pipeline ID; which user data to send to useractuators; which data to save locally; and which data to transmit to thecloud.

The app may include a communication utility 16 which is a switch thatroutes which sensor data is sent to the analyzer for analysis by thepipeline. It also switches which subset of the processed data is sent tothe user interface. Communication utility 16 inputs may include: encodedsensor data, local profile manager, local profile ID, pipeline ID.

Local profile manager 22 may manage the user's profile at the mobileclient device 14.

There may be an interface from the rules engine 26 to each app operatingon the mobile device 14. The rules engine 26 may be an app that runsacross all other apps that helps the user reach some goals.

In a non-limiting exemplary implementation, rules engine 26 may be usedin an audio environment manager. The rules engine 26 may assist inchoosing music based on the user's mood, or determine a user goal basedon what is the person doing (i.e. exercising, working). If exercisingthe goal may be to boost energy. If working the goal may be to cultivatea state of stable high attention.

If the user receives a phone call then the rules engine may switch themusic off or modulate music lower volume of change the frequencyresponse of the music. If the user receives a phone call then the rulesengine 26 may switch the music off or modulate music to a lower volumeof change the frequency response of the music, to reduce stress to theuser. If the phone call is a conference call, the user is wearingearbuds, and a microphone array is present in the room, based on theattention that the user is placing on another participant in the room abeam former chooses which audio components to enhance and which tosuppress in order to maximize the quality of audio received from theperson or device in the room that the user is paying attention to. Thisis useful in an environment where multiple conversations are takingplace and the user has difficulty sorting out the noise from the signalhe/she wants to hear.

The client side rules engine may choose which workflow to apply based onthe conditions that exist when an app is executing. A pipeline may beselected that the analyzer will apply to sensor data of a specific user.The conditions of a rule can include the context of the user (such asbrainstate, activity, prior goals that the user has stated, GPScoordinates, time of day, open apps on the device) to determine how toanalyze the sensor data (such as which algorithm pipeline to apply towhich sensor), and to determine which results to display and/or storelocally on the client and which are stored in the server.

The client side rules engine provides overall management of a person'sexperience and what is learned about them by specifying workflows. Thefollowing example data provides an example of rules in the rules enginethat specify which work flow to use depending on the context that theuser is faced. For example, a Condition might be “Activity Type=WorkingAlone on a Project” and the corresponding Workflow might be “1, EnhanceMental State A (e.g. creativity, attention)”. For the condition“Activity Type=Interacting with Son”, the corresponding workflow mightbe “2. Enhance Mental State B (e.g. empathy, attention)”. For thecondition “Activity Type=Interacting with Person That is Angry”, thecorresponding workflow might be “3. Enhance Mental State C (e.g.mindfulness, stress relief)”

These examples show that the user has different desirable mental statesthat she wants to achieve depending on their context (i.e. condition).When a condition is true, the workflow associated with the condition isexecuted. In these examples, activity type is a field in the user'sprofile that has a series of timestamps associated with that activitytype. The workflow #1 includes real-time elements that specify whichpipeline to use, which information to feedback to the user. In additionworkflow #1, specifies a machine learning approach. It states the methodas to whether supervised learning or unsupervised learning is to beapplied. If supervised learning then workflow #1 also specifies themethod used to label sections of biological signals and the pipeline(s)needed to extract features, the metric to determine performance and anymeasures or signals not to consider in the machine learning workflow.

An example non-limiting message flow involving the client side rulesengine will now be described. First, the user's calendar indicates thatthe user has scheduled time to work alone on a project. When thescheduled time occurs, the calendar on the User's mobile device asks forconfirmation that this task is still occurring. Second, the Userresponds Yes. The User connects the user's EEG headset to the MobileDevice via Bluetooth. Third, the Mobile Device populates the Local UserProfile “Activity Type” for each timestamp associated with this task as“Working Alone on a Project”. The Rules Engine is looked up and there isan entry in the Condition part of the Rules Engine corresponding to“Working Alone on a Project”. The Rules Engine executes Workflow #1.Fourth, Workflow #1 launches an APP on the Mobile Device that givesfeedback to the User when they are in a mental state that maximizescreativity. The APP loads the algorithm pipeline associated withenhanced creativity. While working the User experiences a creativebreakthrough and taps a button in the APP to indicate this. The APPloads the User profile Activity Type with “Creative Breakthrough” at thetimestamp when the User tapped that associated button. The APP transmitscontents of the Local User profile to the User's Cloud Based UserProfile.

Pipeline Analysis Improvement

In one implementation, the system platform implements a series ofdifferent pipelines, and the system platform is configured to operateadaptively based on individual attributes of users by (A) identifying anindividual user and their intended use of the system (e.g. identifyingthe specific application linked to the system that the individualintends to use), (B) accessing the user's profile, (C) accessing rulesfor selecting one or more pipelines in order to improve operation of thesystem based on the profile (for example based on one or more criteriasuch as responsiveness, accuracy in determining a physical or emotionalstate of an individual user, etc.), and (D) applying the selectedpipelines to operation of the system platform for the user. A skilledreader will understand that the one or more pipelines that will provideeffective use of the system by an individual user may vary over time(for example as a user improves their ability to interact with ameditation training application that utilizes the system of the presentinvention). Also, different applications may be linked to the clientcomputing device, and the user may begin using different applications atdifferent times, and data sets may be acquired over time.

One contribution of the invention, is a system platform architecturedesign that expands the ability to extract training data across a rangeof different applications that are linked to the system, and analyze thetraining data and immediately improve the operation of the system for aparticular user based on the resulting analysis data, again across arange of different applications linked to the system. This minimizes theefforts, number of steps, and time required to train differentapplications to function based on relevant features or attributesassociated with an individual user, and also enables the system tocorrectly adapt to changes in these features or attributes over time.These contributions provide an effective solution to obstacles inadoption of applications by users. Prior art systems do not provide amechanism to leverage understanding of an individual, developed throughtheir use of one application, to other applications linked to a BCIsystem. This limits the ability of prior art systems to interact withusers in a seemingly “intuitive” manner, and requires users to in effectduplicate system training efforts. The present system platform on theother hand, through at least the rules engine, reflects understandingwhich may evolve over time of the relationships of training data for oneapplication and the relationships of training data for anotherapplication. This enables the adoption of applications, and thecompletion of the training phase for particular applications, to bestreamlined. User adoption and user engagement may also be improved.

Customization of Pipelines on Per-User Basis

Another method of learning models that can be used by algorithms todrive applications is to customize the algorithms on a user by userbasis. There are two categories of customizing algorithms per user. Onecategory of customization is done with active engagement of the user andthe other does not need active engagement of the user.

Cloud based extensible user profiles for personal information may beprovided. Settings and pipeline parameters that are continuously orperiodically tuned (through the use of the application) to be improvedor optimized for the specific user may be provided. Hardware setups andapplication-specific parameters may be provided. Relevant training datamay also be provided. This will allow application usage to be deviceindependent, and allow for back compatibility as the application isimproved.

The present invention may provide for customization of pipelines withthe user's active engagement. EEG data is acquired and stored in thecloud whenever an application is used. Each session of an applicationhas statistics, results and or scores associated with each session. Amilestone achieved by the user can be defined in a number of differentways such as number of sessions completed, exceeding a scoreconsistently or number of hours in total across all sessions or the useris struggling to reach a milestone. After a milestone is reached, anopportunity to customize their algorithm is offered to the user. If theuser accepts then two (or more) new pipeline variants are generated. Thevariants can be based on new models of new features (such as newfrequency bands) that are correlated to scores. The user then repeats 3brief exercises (e.g. 2 min.) where each exercise uses the 2 newpipelines plus the old pipeline. The user rates each session and thebest performing pipeline as per user preference is kept. The user isasked to rate which pipeline worked best. No final score may bepresented to the user however a running line of the user's score ispresented while the exercise is underway. Objective scores arecalculated and compared to the user's subjective experience. This isrepeated for each new pipeline and existing pipeline. The pipelinepreferred by the user is then kept.

A different option of pipeline evaluation by active user engagement asksthe user to try different strategies to get a high score per exercise.The different strategies can include loving-kindness meditation, breathanchor, counting breaths, body-scan, listening to guided exercises,listening to different types of music, CBT exercises, etc.

The user is asked to rate what strategies worked best. These results arekept in the user profile as features to be used in subsequent algorithmimprovement data mining operations as well as provide relevant data forpopulation demographics related to brain-type and learning strategieswithin the particular applications context. Other users benefit byreceiving more appropriate suggestions to improve their own performancebased on this statistical data.

The present invention may provide for customization of pipelines withoutthe user's active engagement. With this method of evaluating newpipelines, variations are applied to the pipeline to see if and how theuser's performance changes. The pipeline is changed gradually over time.If the user consistently improves then the new pipeline is kept.

An example of personalized pipelines may include adaptive pipelines.Each user's brainwaves are different and therefore require differenttunings for each user. A user's profile consists of brain signaturesunique to each user that have been derived by the pipelines usingreal-time behavioural and environmental information capturedsimultaneously (and time locked) with EEG, as well as the user'sclassification of the user's current mood, emotion, opinion, etc., atthe time the EEG data is acquired.

The combined data may be used in a semi-supervised machine learningprocess to discover unique brain signatures that enable interaction withapplications. The user profile is dynamic and changes over time as moreinformation is gathered and as the user's skill grows. Brain signaturescaptured in a user's profile can be reused in other applications. Alsothe use of other applications are additional opportunities forfine-tuning a user's profile. A user's profile is built so that thelearning curve for interacting with a brainwave enabled application willbe reduced. This will improve adoption and frequency of use of brainwaveenabled applications.

The user profile may speed up the adaptation for an existing user whenthe user uses a new application, through the significant amount of priorknowledge of the user brain characteristics, or brain signature, havebeen collected through the use of other applications.

Machine learning is used to discover brain-signatures on a per personbasis across a number of their sessions. Brain signatures will bediscovered based on features extracted through analyzing EEG signals ofa user participating in prescribed exercises plus the other datamentioned in the paragraph above. Brain signatures will get more andmore refined as the number of sessions that a user participatesincreases. Brain signatures can be used to provide quantitative and orqualitative real-time or post-session feedback to a user in a number ofways such as a score of their skill level or as input to a game thatincreases a gaming parameter in proportion to the brain state at a givenmoment in time (e.g. speed of a race car). It may also be possible toprovide advanced users with feedback on their brain-signatures and howthey relate to the scores they receive.

Before customization on a per user basis can occur, generalizedpipelines are built based on a large number of users. Machine Learningis applied to this data for the classification of brain states throughthe use of labeled data to train algorithms. Once this generalizedpipeline is built it can be adapted to specific users and adapt as theuser's skill improves. Brain training exercises based on evidence foundin the literature and confirmed by testing with in-house experiments areused. User testing is conducted to gather labelled training data. Withtight control of the experimental conditions, the system platform canmap the basic sources of variation to determine the general signalcharacteristics of the target brain states of interest. This allowscreation of models and general foundation algorithms that can seed andstabilize the user specific classification and adaptation methods. Agrowing collection of EEG data in user profiles from increasing numberof apps and users stored in the Cloud will allow further enhancements togeneralized pipelines.

Data that is gathered through app usage is combined with other sourcesof data (biological data and non-biological data such as: behaviouralinformation, demographic information, answers to surveys or formalquestionnaires other user entered text) and will be used to docross-population studies for the refinement of generalized algorithmsacross a large number of users and may be fine-tuned to specificdemographics or based on characterization of brain types using EEGsignal analysis of users doing prescribed exercises (e.g. experiencedmeditator versus novice). Anticipated refinements would yield higherperformance, quicker adaptation, and greater consistency across the userspectrum.

A non-limiting example method for the creation of an initial generalizedpipeline is described. First, controlled experiments are conducted wheretest subjects are subjected to appropriate stimuli and goal orientedtasks, such that their bio-signal data can be labeled.

Second, brain features are computed using pre-processing and featureextraction methods that have been successful in other applications.Third, machine learning methods are employed to discover a generalizedpredictive model.

Pipelines are intended to be used for real-time feedback. Anothernon-limiting exemplary method used is described as follows. First,controlled experiments are conducted where test subjects are subjectedto appropriate stimuli and goal oriented tasks and sham feedback, suchthat their bio-signal data can be labeled. Second, brain features arecomputed using pre-processing and feature extraction methods that havebeen successful in other applications. Third, machine learning methodsare employed to discover a generalized predictive model. Typically anumber of different supervised learning methods to the data features(usually choosing the methods that produce the highest performance usingcross validation). Fourth, apply the classification on-line, with theaddition of similar on-line detection and removal of artifacts from theEEG. The system platform may then replace the sham feedback withfeedback based on the classification and repeat the process.

A brain signature is a set of features that are collected, quantized andstored as a multidimensional histogram, and is part of the user'sprofile, that is used in conjunction with a set of algorithm pipelineparameters to classify the user's state (user response classification).

A covariance matrix of features is a special case of this histogram andis an embodiment of the brain signature. Typically the brain signatureused in the client is a covariance matrix of the subset of features thatare most salient given the computational resources of the client devicethat are available for feature extraction and classification.

For a new user a generic brain signature is used to initialize eachuser's profile and classification parameters. As an individual uses anapplication, their collected features are used to further populate thefeature space, building on the generic algorithm and to allow for theoptimization of their classification parameters.

Updates to user's brain signature can be applied both on the clientdevice as well as on the serve. Updates on the client side are typicallydone on-line to optimize the real-time performance (e.g. to sustain orimprove the performance across a session).

In one embodiment, the system platform may add a forgetting factor tothe histogram accumulation process to allow for adaptation to thecovariant shift that is experienced in the variables as the user learns.In most cases the system platform does not forget the histograminitialization, in order to stabilize the adaptation.

In another embodiment, all of the data that has been transformed intothe histogram space, are stored individually as a multidimensional timeseries, and the covariant shift of these features are is estimated usinga model predication, such as a low-dimensional polynomial model.

Collected user data is aggregated on the server such that a morecomprehensive learning process may be applied such that the user's basemodel (base model is the feature axes of the histogram, or in the caseof a covariance matrix brain signatures, the base model would be the setof features that the client pipeline estimates.) can be updated toimprove the fit of the model to the user. These full data methods arerun on the server because of their complexity and to add value to datauploads for the user. It also allows for interesting analytics to beprovided back to the user through a web-portal.

Pipeline/Algorithm Improver Implementation

FIG. 12 shows an example of a data mining improver/optimizer inaccordance with the present invention. Client side processing of thedata will often be tuned to conserve on compute operations due to thebattery and other resource limitations of mobile devices. Offline cloudmethods will run many different pipelines and select highest performingconfiguration (in terms of classification performance) and providepipeline parameters to the client.

The cloud methods and data storage are optimized to utilize parallelcomputing methods such as map-reduce to parallelize pipelinedevelopment. E.g. Map operations distribute time contiguous raw dataacross workers; these raw segments can then be mapped across featurecomputation workers. The various feature types computer from the data iscollated and redistributed to feature selection and classifier design(machine learning) processes.

Some important features of the non-limiting implementation may include:time code fixer; rules engine; feature extraction; feature selection;supervised machine learning; and parallel methods for cluster/gridcomputing for algorithm improver.

The TIME CODE FIXER/signal synchronizer method may evaluate the timeoffsets needed for synchronization of client devices with the rest ofthe platform and to correct for unknown time offsets and non-uniformsampling.

The RULES ENGINE may supervise (and provide an interface to) theoperation of the data mining methods that are run on the user data.

The FEATURE EXTRACTION may include PREPROCESSING (multiple methodsconsidered to find the most optimal combination); FEATURE EXTRACTORS(Employ more advanced feature methods that may be too complex for clientdevice computation); and UNSUPERVISED MACHINE LEARNING (automatic datadriven feature extraction, Pattern discovery, K-means clustering,Hierarchical Generative Models such as Convolutional Deep BeliefNetwork).

The FEATURE SELECTION may include selection of feature subsets beforemachine learning to improve on convergence and reduce prediction errordue to noise.

The SUPERVISED MACHINE LEARNING may provide for a determination ofcorrect data labelling, which is a critical part of estimating theoptimal pipeline. The multi-method approach that is used for pipelineimproving can generate more accurate (less noisy class labels).Aggregate results (the most likely result based on all methods) are fedback into the Time-Series database in the form of class belief columns,best estimates of class labels fed back through system to denoise data(trim outliers etc. for methods that are prone). The supervised machinelearning may also provide a method for converting computationallyintensive feature methods to pipelines that can be executed on theclient device. Once patterns are discovered, its typical thatcomputationally efficient but adequately performing alternatives can befound. A primary benefit of the more advanced features is to make themachine learning problem more tractable.

The PARALLEL METHODS FOR CLUSTER/GRID COMPUTING FOR ALGORITHM IMPROVERmay provide Methods for operating the pipeline/algorithm improverutilizing parallel computing methods to capitalize on cloud cluster/gridcomputing architecture.

The TIME CODE FIXER may correct time codes. Each computer or device in anetwork may have a time source that is not synchronized with the othercomponents in the network. We want to timestamp every sample (e.g. EEGor ECG or camera or thermometer etc.), each signal can be thought of asa series of samples at a discrete point in time where each sample has acorresponding timestamp. The timestamps across all of these samples fromdifferent sensors need to be synchronized for us to derive correlatedpattern information.

An exemplary non-limiting Message Flow for Timestamp is described, inthe context of an Augmented Reality Example. It may be desirable toestimate the emotional state of the user but using an input from anothersystem. For instance, User A is talking to user B, User B is using avideo camera and is streaming the live feed to the internet. The videofeed is available to user A through the system platform, where videoanalysis can be done by cluster or grid computer. User B is pointing thecamera at user A, and thus the processed video contains features offacial expression of user A as well as features related to their bodylanguage. User A is using the cloud platform to estimate the user's biostate for the purpose of tracking the user's cognitive and affectivestate as a means of ameliorating the user's depressive symptoms. Withthe included access of video features, user A has access to betterbrain-state estimation using the joint signal space. Time stamping (inthe same time base of user A) of the source video and of the derivedfeatures is important to be able to do the joint estimation. The systemplatform may consider the architecture and method so that the above isfeasible in real-time or a near to real-time as possible. For examplethe video features may be available at a later time than the otherfeatures because of the requisite processing and possible internetdelays. At the very least the system platform may have to wait for thevideo, or the system platform may need to be configured to estimateusing what is had, and then improve the estimate when receiving thevideo. This may be similar to doing inertial tracking of video and thendoing drift correct using image based tracking.

Time coding may present various problems. For example, specific sensorsor pipelines (feature or classifier) may have uncertainty in whenexactly things happen. The system platform needs a way of encodinguncertainty in time. Quantization of time may be performed withcoarseness based on uncertainty. The uncertainty window/confidenceinterval may be in the time stamp record.

Different data sources may also have different times. The systemplatform may synchronize clocks by having components of the systemplatform send times back and forth until an adequate estimate of thenetwork delay can be estimated and then the system platform can estimatethe clock difference (for example, by NTP or computation the round-tripdelay time and the offset). Given a history of clock offset measurementsbetween two computers, the timestamp of a remotely collected sample canbe remapped into the mutual time domain.

While the system platform may not have a way of keeping time, the systemplatform may perform synchronization later based on information in thesignal (for example where there is EEG data of a user's blinking andvideo data of the user's face with blinking is also provided, the systemplatform may sync the video to the EEG data).

Non-uniform sampling, and missed samples may occur. Missed samples arefixed using packet numbers if they exist, otherwise advanced methodsneed to be used. In some cases, like where data has chunks missing, wemay use computation of a simple time shift use Euclidean distance overthe time series. This may be done by doing window based Euclidiandistance minimization, using cross correlation. Subdivision methods maybe used by the system platform to narrow into finer timescales.

In more difficult cases of non-uniform sampling, the system platform mayuse dynamic time warping (though other methods are possible). A dynamictime warping (“DTW”) algorithm may calculate the distance between eachassociated pairs of two sequences in order to minimize the distance. Tomanipulate this algorithm, dynamic programming is applied to find theleast expensive path through a cumulative distance matrix. Formulti-dimensional data, the system platform may use multidimensionaldynamic time warping (“MM-DTW”). This pipeline utilizes all dimensionsto obtain the optimal path and aligns multiple signals simultaneously.After alignment, the regular similarity measurement can be used to thesealigned signals (see Parinya Sanguansat, “Multiple MultidimensionalSequence Alignment Using Generalized Dynamic Time Warping”, WSEASTRANSACTIONS on MATHEMATICS, Issue 8, Volume 11, August 2012, web:http://www.wseas.orq/multimedia/journals/mathematics/2012/55-273.pdf,the entirety of which is incorporated by reference).

Cloud Side Rules Engine

The cloud side rules engine is designed to: 1. to improve the pipelinesused in specific applications for specific users; 2. to do crosspopulational data mining to improve general application performance anduser experience across the entire user base and for new users; and 3. todo cross populational data mining for potential new application or toserve a research study (internal or third party, while maintaining userprivacy).

The cloud side rules engine may comprise: high level code thatsupervises the operation of the data mining methods that are run on theuser data; a database of rules that determine which Workflow to applybased on the conditions (e.g. where a rule has the following structure:IF condition==true then execute Workflow ID); and an API that allowshuman researchers to guide the data mining process. The cloud side rulesengine communicates with the profile manager to update rules andpipeline configurations in client device applications.

Workflows in the cloud can apply machine learning methods that can learnnew pipelines, change which pipelines to apply to specific contexts,learn new patterns, learn new patterns that can help a User achievetheir goals. Rules that control machine learning choose workflows thatapply different Machine Learning methods depending on the conditionsfaced by the user. A different machine learning workflow can be definedthat chooses different classes to learn to classify and the specificfeature extraction and feature selection methods to apply. When amachine learning workflow has discovered a new predictive model orclassification pipeline, the rules engine can direct the updating of newpipelines the apps that can use the pipeline and the contexts that thesepipeline contexts can apply.

Workflows that include machine learning and other statistical methodsthat determine if specific rules are effective and need to be updated aswell as the updating process (example of process are, discretechanges—that may include user notifications, continuous adaptations likeresponse speed, or A/B testing changes whereby variations to rules aretested with a user to see if the behaviour is desired. Variationtechnique may involve stochastic processes like genetic algorithms, oruse statistical methods such as hidden markov models with baum-welchtraining.

The Following is an example of how the Cloud Based Rules Engine updatesa pipeline based on a significant event “Creative Breakthrough” achievedby the user. For the condition “Activity Type=Working Alone on aProject”, the corresponding workflow may be “4. Use unsupervisedlearning to find statistically significant patterns associated with timestamps of this User. Wait until end of this Activity Type or CreativeBreakthrough to process”. For the condition “Activity Type=Creativebreakthrough”, the corresponding workflow may be “5. Use supervisedlearning”. For the condition “Activity Type=Statistically SignificantPatterns Discovered”, the corresponding workflow may be “6. Alert Userthat new patterns are available for review”. For the condition “ActivityType=Better Prediction Model Discovered for Mental State=Creative”, thecorresponding workflow may be “7. Update all APPS using pipeline ID withnew parameters of the Better Prediction Model, and transmit newparameters associated with pipeline ID User's Local Profile”.

In this example, first, the Cloud User Profile may receive an updatedUser Profile from Mobile APP. The Rules Engine in the Cloud sees thatActivity Type=“Working Alone on A Project” and waits until end of thisactivity to start looking for patterns. Second, The Rules Engine in theCloud sees that Activity Type=“Creative Breakthrough” and searches alldata associated with Activity Type of that User just prior to theCreative Breakthrough and use patterns learned from previousunsupervised and supervised sessions across all APPs as input toSupervised Learning. Supervised Learning done in the Cloud discovers aPrediction Model that has higher accuracy than the previous predictionmodel. The Rules Engine sets Field “Machine Learning ActivityType=“Better Prediction Model Discovered”. The Rules Engine starts aWorkflow that updates the pipeline ID with the parameters learned. TheWorkflow updates the User's Profile with the new parameters in thePipeline ID. Third, the Cloud transmits the new parameters of thePipeline ID to all Mobile device APPs using that Pipeline. In thefuture, app IDs that are associated with this Pipeline ID will use thenew parameters.

When the rules engine yields improved pipelines, these pipelines aremade available to the client devices through providing the pipeline IDalong with the associated parameters that the pipeline requires. Allpipelines are available on the device and can be switched betweenpipeline ID by instruction by message from the Cloud with associatedpipeline parameters. The Cloud Server analyzes each Activity Type anddetermines which pipeline to choose for this user or activity. Thepipeline chosen may also depend on other information gathered fromuser's profile such as age and or as a direct response to surveyquestions or if a user suffers from a particular disorder. Otherinformation that is used to fine-tune the pipeline is a history of thesessions of the user:number of sessions, total number of session hours,time of day of session, session scores and results. Machine learning isused to determine patterns across a user's sessions to change and adapta user's pipeline. The Cloud Server transmits the pipeline ID to thedevice along with the learned parameters. In addition, the specificparameters of the pipeline can be determined within the device ifdisconnected from the Cloud. If this is a first time user of the an APPthen a default pipeline is chosen for the user. Again, default pipelinesmay be fine-tuned based on demographic or survey responses. The user canbe part of an evaluation to select among a number of differentpipelines.

Feature Extractor Designer

The feature extractor designer is comprised of a set of pre-processingalgorithms that are used to filter, normalize, or otherwise transformthe raw time-series data into a related time-series that is moresuitable for different types of feature extraction. The cloud basedrules engine defines the learning problem and informs the FeatureExtractor Designer of the prior knowledge of what combinations ofPre-Processing and Feature Extractors are to be considered. Thepopulations of combinations are computed in parallel. Tuneableparameters and alternative Pre-processing and Feature Extractorcombinations are selected using uniform or stochastic sampling within arange, genetic algorithm or a gradient method depending on theparticular pair of algorithms.

The server's pipeline improver houses an array of pre-processing methodswhich are available to the client device. blind source separationmethods are considered pre-processing as the end up producing filtersthat can be applied to the multivariate time-series data, however theytend to be used slightly differently than on the client device due tothe availability of data.

The Rules engine defines the learning problem and informs the FeatureExtractor Designer of the prior knowledge of what Pre-Processing shouldbe considered.

The server's pipeline improver houses an array of Feature Estimationmethods which are a superset of the methods available for the clientdevice.

More advanced feature methods are included that may be too complex forclient device computation. These highly advanced methods can yieldhighly discriminative features that can yield much better machinelearning performance through the creation of over complete features thatcan yield better separability, and reduced search spaces throughfiltering or built in invariances (such as translation invariance).Resulting discovered patterns can then be discovered in the data usingsimpler methods once we know what we are looking for. Two methods thatare utilized include (not exhaustive): Emprical Mode Decomposition (EMD)or Ensemble EMD (EEMD) (see: Zhaohua Wu and Norden E. Huang “EnsembleEmpirical Mode Decomposition: A Noise-Assisted Data Analysis Method”,Advances in Adaptive Data Analysis, Vol. 1, No. 1 (2009) 1-41, web:http://perso.ens-lyon.fr/pierre.borgnat/MASTER2/EEMD.pdf, the entiretyof which is incorporated by reference); and Scattering Wavelet transform(see Stephane Mallat, “Group Invariant Scattering”, 15 Apr. 2012, web:http://arxiv.org/pdf/1101.2286.pdf, the entirety of which isincorporated by reference).

Unsupervised Machine Learning for Patterns of a Single User

The server may constantly be calculating correlations between differentusers or between different data for a particular user. A rules enginewill determine what the server should work on so that the server's timeis efficiently managed.

Automated searching for meaningful patterns or associations in datausually requires a human to create a statistical analysis plan withhypothesis and execute the analysis off-line in statistical softwareprogram. In accordance with an aspect of the present invention, thismethod automatically searches the data in the cloud platform or on auser's device for meaningful patterns without any prior hypotheses inmind. Biological and non-biological data can be searched to findmeaningful patterns about a specific individuals or across population ofindividuals.

A meaningful pattern is a pattern identified in a user's bio-signal datathat represents a user's response observed to particular events (whichmay be called “feature events”), particularly when that user's responseis observed multiple times with respect to the repeated occurrence ofthose particular events. Searching for meaningful patterns may be usedto predict whether a feature event belongs to one category or another.For example, a meaningful pattern may occur when the value of eachfeature event (A, B, C) occurs together more often than just randomchance. The patterns may also reveal new insights into behaviour. “e.g.I am always anxious on Sunday evenings”, or they may suggest whole newcategories and cluster them together. A meaningful pattern can be usedto (i) build prediction model (ii) to select features. Once a meaningfulpattern has been discovered, the system platform may modify or create apipeline to predict one of the feature event values from the other two.So if the system platform is provided with the values of A and B thesystem platform may be able to use the pipeline to determine theprobability of the value of C. A meaningful pattern may be determinedacross individual users or across multiple or all users. Any time ameaningful pattern is determined for any user, any such pipelinedeveloped may be available to other users or applied to bio-signal dataof other users.

Feature event data (including identifications of feature events, andrespective values) may be inputted manually by a respective user, orthey may be sourced from elsewhere, such as from a calendar applicationor emails accessible from the client computing device pertaining to theuser. For analysis purposes by the system platform, it may besignificant that the feature events occur together usually in a point intime or occur together at a place. Therefore, the feature event data maybe time-coded, optionally by the system platform, in order to referenceagainst bio-signal and/or non-bio-signal data of the user with the sameor similar time codes. A feature event is all of the variables that gointo getting a single data point of multiple variables. Features can bederived from any measure or variable that is available to the systemplatform, such as time of day, EEG signal, heart rate, person's mood,age, gender, height, weight education, income, etc. The system platformmay maintain a database store of all of the combination of featureevents. When the system platform performs a prediction the systemplatform finds matching patterns and uses the highest order featureevents first to do the classification.

The system platform typically may look for at least 10 data points forevery feature and for every category. For example, if the systemplatform has three categories (e.g. drowsy, alert, agitated) and tenmeasurements per data point then the system platform may require atleast 3*10*10 data points (i.e. 300 rows and 11 (10measurements+Category) columns). The rules engine can enforce this kindof policy so that system platform only begins looking for a new patternin feature event data when there is a particular amount of data toanalyze above a particular data amount threshold.

A method for searching for meaningful patterns is shown in FIG. 10. Adata set with features will be searched for patterns that may provideinsights that are meaningful for individuals, or across populations orcommon for specific demographics or for people with the same disorder.These patterns may also be used as rules for classification. The Cloudplatform has acquired data from a number of different applications thatthe user has used. Example, one application uses EEG recording of asingle session. A session may consist of a number of parts or epochs. Asession or epoch is described by a number of features. Features arecalculated from biological and non-biological data. Feature values canbe continuous variables (e.g. EEG alpha power), ordered (e.g. small,medium, big) or categorical (e.g. ADHD, Normal). Values of continuousvariables are quantized (i.e. binned) into a set of discrete bins. Thereare situations where it would be valuable to consider other observationslike nominal features such as gender or hair colour along with numericalvalues to build Predictive Models. Quantization allows nominal andcontinuous variables to be considered simultaneously. Each feature cantake on a discrete value from an integer number of total values for thatfeature. A primary feature event occurs when a single feature takes on adiscrete value. A kth order feature-event contains k primaryfeature-events. An example of a 4th order feature-event would be from asession where the person is 1) moderately relaxed 2) heart ratevariability is low, 3) alpha power is high and 4) alpha variability ismedium. The next step is to determine if the feature-event is astatistically significant pattern. There are a number of statisticaltests available to determine statistical significance such as Chi-squareor adjusted residual. Tests of significance are two-tailed in that asignificant pattern may occur if there are more than a random number ofepochs or sessions with the same feature-event or less than a randomnumber of epochs. Significant patterns can be used to predict acategory. For instance a session that had low heart rate variability,high alpha power and medium alpha variability can predict that a personwas moderately relaxed. The strength of a pattern is determined by theprobability of its occurrence. These reports of patterns can be used forclassification or prediction. Individuals can be alerted to theexistence of significant patterns that may be informative for anindividual to understand their patterns and help them reach their goals.

The system platform may determine which bin a time-coded portion of anEEG signal may be associated with by evaluating the value of medianvoltage of the EEG signal.

When the system platform is provided with an EEG signal that can be usedfor training or to learn a model, the system platform could process thesignal to remove unwanted artifacts. The system platform may then takeslices in time across the entire EEG signal (e.g. at every 10 ms). Thesystem platform may calculate the median voltage within every sliceacross the entire EEG signal. The system platform may sort these valuesfrom low to high. For example, where there are 10,000 slices, the systemplatform may use 10 bins, looking at the ordered 10,000 values anddividing them into 10 parts. Then the boundary of the first bin may bedetermined by a voltage value of 1000 or less, while the second binboundary may be determined by a voltage value in the range of 1001-2000,etc.

In a non-limiting exemplary implementation, discovering meaningfulpatterns may be performed as follows. Assume a user “Charlie” having twofriends “Andrew” and “Bob”. Charlie has a database of outings of himselfand his friends including information regarding whether Andrew and Bobgo out without Charlie, which has been reported to Charlie. The databasehas information about the outings such as venue, date, type of activity,whether the outing was enjoyable etc. Charlie wants to analyze the datato find patterns. To simplify the example, focus on two features: 1)whether or not a friend was present and 2) whether or not Charlieenjoyed the outing. There are six primary feature-events in thisexample: A=Present (Pr), or A=Absent (Ab); or B=Pr, or B=Ab; orC=Enjoyed(E), or C=Not enjoyed(N). An example of a second orderfeature-event in this example may be A=Pr, B=Ab. An example of a thirdorder feature-event in this example may be A=Pr, B=Ab, C=E.

The table shown in FIG. 11A shows all permutations of second orderfeature-events of Andrew, Bob and Charlie that occurred over a total of102 outings. The column ox is the observed number of these second orderfeature-events across all 102 outings. The column ex is the expectednumber based on assuming that the features are statisticallyindependent. The column dx is adjusted residual a statistical test thatdetermines if the observed number is statistically significantdifference from the expected number: None of the second order candidatefeature-events are statistically significant and therefore notconsidered patterns.

The table shown in FIG. 11B shows that four significant 3rd orderfeature events are statistically significant patterns. The first patternreveals that if both Andrew and Bob are absent then Charlie does notenjoy the outing. The second pattern reveals that if both Andrew and Bobare present at an outing then Charlie does not enjoy the outing. Thethird pattern shows that if Andrew is present and Bob is not thenCharlie enjoys the outing. The fourth pattern shows that if Andrew isabsent and Bob is present then Charlie enjoys the outing. Therefore ifone but not both of Andrew and Bob are present then Charlie enjoys theouting. This pattern helps Charlie plan future outings.

In this example, the expected numbers may be determined by assuming thatthere is no relationship between the features. So in the example, thesystem platform would determine the expected value by multiplying theprobability of each event occurring on its own together and thenmultiply by the total of data point. ExampleP(A=Ab)*P(B=Ab)*P(C=No)*Total # Datapoints=13.

The above example shows the power of considering features simultaneouslyfor discovering patterns. A search across two features revealed nopatterns, however, searching across three features simultaneouslyrevealed significant patterns.

Unsupervised Machine Learning for Patterns Across Multiple Users

After EEG raw data is transmitted to the Cloud, as described previouslyunder the heading Mobile Device Based Application—Processing at CloudServer, EEG data from a large number of users may be accumulated in theCloud Server. EEG feature values plus other information stored in eachUser Profile is discretized. These discretized features are sent to theMachine Learning module for automated pattern searching. This may be anoffboard server that may have dedicated specialized hardware to processthis process intensive search.

All permutations of patterns up to a certain number of simultaneousfeatures are searched for statistically significant patterns (i.e. go ashigh as fourth order feature-events).

A database of significant patterns (or user-response classifications) isaccumulated in a pattern database in the Machine Learning module of thesystem platform. These patterns are shared with users that exhibit thesepatterns. A particular user may be asked if the shared pattern is ofinterest to that user. If the user responds yes, the Machine Learningmodule sends feature detection thresholds and prediction model to theuser's mobile device. The mobile device processes EEG signal andprovides predictions to the user. The user may respond to thepredictions as to their accuracy or lack thereof. The revised userfeedback may be used to update how segments of EEG or biological signaldata is labelled, thereby updating and improving the information stored.

The search for meaningful patterns continues, and a second level ofmachine learning can be applied to the discovered patterns guided byhuman knowledge of by a second level of supervised learning across thediscovered patterns.

Feature Selection

Feature selection involves searching for features that can providemeaningful information towards the prediction at hand. Sets of featurescan be selected that together contribute to improving predictionaccuracy when testing against data. Another way of selecting features isto systematically search for features that have a statisticallysignificant relationship with a dependent variable or category.

Feature selection algorithms may include: Stepwise Logistic regression;Stepwise Linear Regression; minimum redundancy maximum relevance (mRMR);and genetic algorithms.

Feature selection is typically done using a wrapper method wheredifferent permutations of feature selections are used to train aclassifier such a support vector machine with a non-linear kernel. Oneembodiment may use a genetic algorithm to permute the features, and theresulting classification accuracy of the trained classifier is used tojudge the fitness of each selection (fitness then being used as part ofthe selection process for future generations, as may be typical forgenetic algorithms). Parallel forms of a genetic algorithm may also beused to distribute the computational load across a cluster or gridimplementation of the system platform.

Constraints relating to the real-time computational resources areintroduced into the feature selection process to ensure that theresulting real-time estimators are runnable on the target platform.Parameters may be be platform dependent. For example, an applicationrunning on an iphone3 may have a different constraints than and iphone4which will have different characteristics than a desktop computer.

Supervised Machine Learning

In the embodiment as shown in FIG. 5, the feature extractor and theclassifier can be implemented in a supervised machine learningenvironment 700.

For example, training data 703 can be sent to the feature extractor 701and classifier 702 for analysis, and the results of the analysis can beoutput to a supervisor for review (for example using a suitablegraphical user interface such as a dashboard). In this example, asupervisor may be a feedback mechanism whereby a person can decidewhether the analysis has correctly identified a feature associated witha particular pattern. In another aspect, the computer system may includea rules engine that includes a series of rules for guiding a supervisorin deciding whether the analysis has correctly identified a featureassociated with a particular pattern, depending on a defined context,and optionally also depending on parameters associated with thebio-signal data and the non-bio-signal data. The rules engine may belinked to a suggestion engine that supports the display of one or moremessages to the user to guide the profile development process, and/orsystem calibration processes.

The rules engine may allow the feature processor to know whichdimensions to prioritize, and may reduce the amount of signal bandwidththat is necessary for transmitting. The feature space or dimensions maybe dependent on the context. The rules engine may be considered to be acontext based switcher, and as such may use the outputs of differentestimators (i.e. is the user sitting in front of their computer; is theuser working; what is the user hearing, etc.).

The context estimator can be considered to be part of the rules engine.It may run in closed loop fashion so that context is estimated withfeedback. The context estimator may yield more accurate estimates thatare created using better information or the results of certainalgorithms that take time or additional information to run will need tobe applied back in time. A timestamp may be used to mark the event ofthe new context estimate. Estimated quantities in the time seriesdatabase (such as context or user's mental state) are included as rowswhile pipeline ID and configuration parameters are included in thecolumns. Estimated quantities can thus be traced back to their origin,and thus provides a means of determining if they could be updatedbecause of a change in their data sources or algorithms. This couldcreate feedback loops, and care must be taken to ensure stableoperation. Stability of the estimation is monitored by and algorithmthat operates on past classifications of the data which are accessedusing the timestamp column of the data hypertable. A new row may becreated with each modification to the context estimate marked with atimestamp corresponding the time that the new estimate was created. Thecontext estimator may assess the importance of source data and also toestimate a confidence interval (i.e. value, uncertainty, and sensitivityto other inputs). The stability of recursive context estimation issensitive because of how its effects propagate. To compliment recursive(sequential) estimation, concurrent (parallel) estimation using randomsampling from prior distributions of context states may be used in orderto yield stable and accurate estimates.

In another aspect of the present invention, the rules engine itself islinked to the machine learning systems and methods that are made part ofthe system of the present invention, so that the processes implementedby the rules engine (for example for enabling users to develop theirprofiles and enable calibration of the system based on training data703) may be modified iteratively based on learning developed by thesystem by engaging with a plurality of users and optionally a pluralityof groups of users that share attributes in the manner in which theyinteract with the system and/or particular applications linked to thesystem.

For example, a user of the client device may be asked whether aparticular classification corresponds with a particular emotion orphysical movement experienced at the time the bio-signal data andnon-bio-signal data was collected. This may be useful where there ismore than one pattern or correlation for a physical or emotional state.A skilled person would understand that other ways to supervise machinelearning may be employed.

The results of the extraction and classification, after feedback fromthe supervisor, may then be used to improve the feature extractor 705and classifier algorithms 706. These tests may be repeated in order totrain the machine learning system such that, for example, by using oneor more known statistical models for analyzing bio-signal andnon-bio-signal data with greater accuracy 708 may be achieved. Forexample, a mixture of Gaussian distribution models may be used.

The bio-signal and non-bio-signal data, before or after processing andanalysis, may then be associated with a particular user of the system,for example using a profile builder associated with the system. Aprofile builder may be linked to a database and may embody one or moreprocesses for defining for each user a set of user profile data 114.This user profile data 114 may also include, for example, data fromdifferent biofeedback applications, data from different sensors, datafrom different client devices, and user demographic data. Bio-signal andnon-bio-signal data may be different for different users. Thus,optionally, algorithms 116 used in the pre-processing, featureextraction, and classification of data may also be adjusted for eachuser, e.g. based on preferences or characteristics of the user.

Different profiles may exist for an individual user for example fordifferent applications that link to the system of the present invention.User profiles may define different attributes in a number of differentways, and these attributes may be reflected in profiles in a number ofdifferent ways. For example, users of a particular application linked tothe system (such as a meditation training application) may be classifiedbased on a range of values associated with their experience of certainmeditation induced states. A representative profile may thereforeinclude an identifier that is associated with a state classificationprofile that best matches the individual user. This system may detectthat over time the user's interactions with the application are changing(for example because the user's medication training efforts have beeneffective). This may be detected by the system, and the system maydynamically adjust the user's profile. Also, the machine learning systemmay detect and generate insights regarding improving the classificationof individuals based on new understanding of interaction of usersassociated with particular attributes with selected functions of anapplication, and the creation and use of profiles by the system may beautomatically adjusted based on these insights.

For example, in one implementation of the invention, an analytics enginemay be used to analyze changes in user features or attributes, andautomatically integrate such changes by updating the user's profile, soas to provide automated calibration of the operation of the system ininteracting with users, on a user by user basis.

The present system may also utilize segmentation techniques to groupusers into user segments based on shared attributes, and reflect suchuser segments in management of user profiles.

There are many unknown associations of EEG signals that can be learnedabout humans. The system platform, and the cloud in particular may beconfigured to continuously, periodically, or in any other scheduledmanner look for meaningful patterns or to update classification models.Two of the major challenges for classifying EEG signals is thedifficulty in obtaining accurate labelled segments of EEG signals andthe other challenge is to select features of the EEG signals that canprovide meaningful information for the classification task at hand. Thissystem platform uses non-EEG data to help label EEG data acquired fromusers undergoing a specific activity or exercise. Non-biological andother biological features across a number of users may be analyzed usingpattern discovery, cluster analysis, or factor analysis along withdimension reduction methods such as Principle Components analysis toplot these features in reduced dimensions. Rules, thresholds orboundaries of these plotted reduced dimensionality of features are setand a label or score is established for each person or each session ofinterest that is related to a goal that the user is trying to achieve.This score can be thought of as the dependent variable (i.e. y-value inlinear regression) that can be used for supervised learning of the EEGdata. Features are extracted from the EEG signals with sessions labelledas per the score or region derived from other biological andnon-biological data. The reduced dimension space allows searching foroptimal classification accuracy easier by adjusting thresholds,relabeling sessions and re-calculating classification accuracy. Thereduced dimension space also allows for more efficient searching to findbetter performing classification models. A superior classification modeleither has higher accuracy or requires lower number of features (whichmay mean more efficient processing) compared to other models. A numberof iterations of a supervised learning method is used to build a newmodel for classifying EEG signals into categories or predicting scoresmeaningful for a particular goal. A model fit parameter is calculatedand checked against previous iterations of model fit or against a modelfit threshold. If the model has not converged then new thresholds orboundaries are chosen to establish scores of the non-EEG data, then anew model is learned using the new thresholds. If the model hasconverged either because model fit parameters have plateaued or exceededa threshold then the model building iterations stop. A decision is madeto keep the new model or abandon the new model because it has notexceeded the performance of the existing model.

FIG. 9, shows a relaxation embodiment of enhanced machine learningshowing how enhanced machine learning can be applied to the goal ofhelping a user reach a deep state of relaxation while remaining alert.The user may want to learn to achieve a deep state of relaxation whileremaining awake and focused to help the user improve the user'sperformance for a number of activities that can benefit from asimultaneous state of relaxation and focus like golf or archery. Non-EEGbiological data and non-biological data is gathered across a number ofusers trying to achieve a state of deep relaxation and focus while theirEEG signals are being recorded. Biological features such as Heart RateVariability, accelerometer readings of motion, level of muscle tensionrecorded by analyzing EMG signals of facial or body muscles as well aseye movement are recorded with timestamps that are synchronized to theEEG recording while users engage in relaxation/focus exercises. Measuresof focus are obtained from the user such as subjective ratings ofalertness from the user or from tests like Tests of Variables ofAttention (TOVA). Factor analysis is used to reduce the dimensionalityof the non-EEG features into two dimensional space as shown in FIG. 9. Ahigher number of dimensions is possible but requires an additionalthreshold per dimension. One dimension is associated with features thatdescribe level of relaxation and the other dimension is associated withfeatures of focus. A threshold is selected along each dimension and thetwo dimensional space is divided into four quadrants: relaxed-focused,relaxed-unfocused, agitated-focused, and agitated-unfocused. Featuresextracted from segments of EEG data are labelled with one of thesequadrant labels that occurred during the same time segment as thenon-EEG data. A learning algorithm such as Linear Discriminant Analysis(LDA) is applied to the EEG feature data that is separated into trainingand test data. The accuracy of classifying the test samples isdetermined. Another set of thresholds is set. Feature data is labelledinto one of the quadrants. An LDA model is learned and the model'saccuracy to classify the test feature data is determined. When theaccuracy of the models converge and no additional improvement occurs,then the searching stops. If the model has improved accuracy over oldermodels then the new model may be used for training of relaxation andfocus. This method can be adapted by using different EEG features foranalysis. The resulting classification model can then rely solely on EEGsignals without extracting the additional biological or non-biologicaldata.

Another embodiment of the Enhanced Machine Learning method is to helpfind a state of flow for a user undergoing a training application byadjusting the level of challenge presented to the user. The user isasked to answer questions about the training activity such as the user'sperceived level of skill, and how challenging was the training activityand emotion experienced during training (e.g. boredom, anxiety, state offlow etc.). Any grades related to training assignments or test scoresare also included as features. These features are input to a dimensionreduction algorithm and then two thresholds are selected where onedimension may be interpreted as the level of the user's skill while theother dimension is considered the level of challenge placed on the user.These are used to label the EEG data recorded during the trainingsessions as one of four possible labels: low-skill/low-challenge,low-skill/high-challenge, high-skill/low-challenge, andhigh-skill//high-challenge. Patterns of EEG features are discoveredassociated with each quadrant. These learned patterns or rules can beused for future training sessions to automatically adjust the challengelevel of the training depending on the EEG patterns exhibited by theuser and help foster a state of flow that the user enjoys whilelearning.

The user's data can be processed and analyzed on the client device, orthe data can be uploaded to a cloud database for processing andanalysis.

There are classes of biological signals that do not require a highbandwidth of information to be transmitted since the biological signalprocessing is mature. For example, Heart Rate Variability is animportant physiological measure that is related to emotional arousal,anxiety, time pressure strain and focussed attention. Heart RateVariability only requires inter-beat intervals to be analyzed whichrequires only bytes of data every second. In addition to EEG signals,muscle tension can be derived from EMG signals measured by forehead EEGelectrodes. Analyzing and processing these signals in a mobile device oreven desktop PC would tax the resources of the device. The followingmessage flow describes how Machine Learning is spread across the deviceand the Cloud to take advantage of the larger processing capacity ofservers in the Cloud offloading this kind of processing from the mobiledevice.

Message Flow for Supervised Machine Learning for Single Person MachineLearning

The present invention may allow collection of time synchronizedbiological data from a number of sensors within or attached to thedevice and apply machine learning to the multiple sources of data in thecloud. In addition, predictions learned in the Cloud can be sent back tothe device so the user can enhance the Machine Learning algorithm, whichmay provide for the user to learn about himself or herself and enhancelearning of prediction models. This message flow describes anapplication that uses Heart Rate Variability and facial muscle activityto determine the level of a person's emotional arousal continuously anduse this information to label sections of EEG data. Machine Learning isapplied to the EEG signal to create a model for predicting emotionalarousal based on EEG signals alone.

After EEG raw data is transmitted to the Cloud, as described previouslyunder the heading Mobile Device Based Application—Processing at CloudServer, the user may set the User Profile to “Learn emotions from EEG”.A photoplethysmography sensor is connected to the mobile device.Interbeat intervals or time stamps of R signals detected from the heartare transmitted to the cloud server User's Profile.

The Cloud server selects a pipeline appropriate to the task of learningemotions. Filters and artifact detection methods are set to separate outEMG data from EEG data. Features are extracted from the interbeatinterval such as frequency information (power spectral densityinformation) determined from the heart rate interbeat intervals.Features are also extracted from EMG such as spatial information, firingrate and signal intensity. Factor analysis is used to reduce thedimensionality of these features into a lower dimensional space. Data iscollected for several seconds to a few minutes. The machine learningrules engine sets thresholds on the reduced dimension space. Based onthese thresholds, an estimate of the person's emotional state isdetermined.

This emotional estimate is sent to the device, which may prompt the userto answer a question such as: “Based on muscle tension and heart rate,the cloud server estimates that you are under moderate stress. What isyour emotional state?” The user may respond “Low, Medium or HighStress”. The user's response is sent to the Cloud Machine Learningmodule.

The Machine Learning module of the system platform adjusts thethresholds based on the user's information. The user's heart rate andEMG features are used to determine if the user is under low, medium orhigh stress. This is used to label segments of EEG data. The machinelearning module then uses these labels to extract features from the EEGsignal and learn a model that can predict emotions directly from the EEGsignal without processing additional data.

The steps of the machine learning module determining the emotionalstate, prompting the user to confirm, and adjusting the machine learningmodule thresholds may be repeated to refine the learned prediction modelthat includes the user in the loop to evaluate models learned by theCloud Machine Learning.

The EEG based emotion prediction model (pipeline) is sent to the device.

Since the prediction model has less processing demands compared tolearning, the device can start providing emotion predictions to theuser.

Example Pipeline: Common Spatial Pattern (CSP) Pipeline

This is an example of using a pipeline to categorize whether a personhas a busy mind or quiet mind, called a common spatial pattern (“CSP”)pipeline. The CSP pipeline may have value as mindfulness based practice(i.e. quiet mind) and has peer reviewed evidence that supports healthand psychological benefits to users that practice mindfulness.

This example shows a pipeline customized to a particular user. The samesteps below can also be done across users for training pipelineparameters.

A User is asked to calibrate (i.e. train) the pipeline that will providefeedback when the user has a busy mind compared to quiet mind. A userwears an EEG headband that records 4 channels (left and right forehead,left and right mastoids) at 500 samples per second. The user undergoestwo calibration sessions: (a) a 4 minute recording that asks the user todo mental math (e.g. count backwards by 7 from 383) to induce a busymind; and (b) a 4 minute recording that uses progressive relaxation tohave a quiet mind. Session (a) provides training (i.e. calibration) datafor Category 1=“Busy Mind”. Session (b) provides training (i.e.calibration) for Category 2=“Quiet Mind”. The Mobile device sends theEEG data of both sessions to the cloud to learn a model.

The User is given instructions for the next part of the exercise whilethe Cloud learns the parameters of a pipeline with the following steps.The Cloud chooses the default parameters by information stored in theCloud for busy mind and quiet mind algorithms. The Cloud separates thesessions into 3 minutes for training and 1 minute per testing thePrediction Model.

An infinite impulse response bandpass filter is applied. For example, aButterworth Filter starts with the default settings of 1−2 Hz for thelower stopband, 2 to 28 Hz being the passband, and 28 to 32 Hz for theupper stopband. At the stopband frequencies of 1 and 32 Hz the signal isattenuated by −50 dB. The ripple of 0.5 dB above the average passbandgain occurs at 2 and 28 Hz. The EEG signals are divided into epochs of 5seconds long for the Busy Mind and Quiet Mind sessions. There are 36epochs (3 min*60 sec/5 sec) per training session. Each training epochhas 2500 voltage samples (500 samples/sec*5 sec).

A matrix is built for Busy Mind “BM” that is 4 rows by 90,000 voltagesamples. Each row is the voltage per EEG signal. Another matrix “QM” isbuilt for Quiet Mind of the same size 4×90,000.

The covariance matrix is calculated for each category byCOVBM=E[(BM−E[BM])*transpose((BM−E[BM]))] andCOVQM=E[(QM−E[QM])*transpose((QM−E[QM]))].

An eigendecomposition is done to get the eigenvalue (D) and eigenvectors(V) by solving the following formulae: COVBM*V=D*(COVBM+COVQM)*V. V is a4 by 4 matrix of transformation vectors. D is a diagonal matrix with 4eigenvalues in the diagonal.

The features for each epoch t are calculated by taking the variance ofsignal per EEG electrode and multiply by V and take the log. Sofeatures(epoch time t)=log(var(EEGsignals(t))*V). This may provide fourvalues per epoch time t per Category. FeatsBM stores the 4 features perepoch of Busy Mind and FeatsQM stores the 4 features of Quiet Mind.Another way to think of these features is that one epoch is a pointplotted in four dimensional space where each axis is one feature.

In this case the system platform performs feature selection using LinearDiscriminant Analysis (“LDA”). LDA weighs the contribution of eachfeature value that will maximize the distance of point in the BusyMindCategory to Quiet Mind. LDA will plot points of each category along aone dimensional line (for two classes) that will be Gaussian since thevoltage values of EEG are generally Gaussian calculated byweights=inv(FeatsBM+FeatsQM)*(mean(FeatsBM)−mean(FeatsQM)). Then theweighted or selected features will be calculated byyBM=transpose(weights)*FeatsBM and yQM=transpose(weights)*FeatsQM.

The 1 minute portion of the session that was withheld for testing is nowdivided into 5 second epochs and the parameters (IIR filter, V, LDA)determined above are applied to the BusyMind and QuietMind epochs. Thesignal of each EEG channel is IIR filtered using the same parameters.The features are calculated using V discovered in the training dataFeatstestBM=log(var(EEGBMtestsignals(t))*V) andFeatstestQM=log(var(EEGQMtestsignals(t))*V). Then the weights discoveredby LDA are applied to the test data by ytestBM=transpose(weights)FeatstestBM and ytestQM=transpose(weights)*FeatstestQM. The distance ofeach feature in the test data is compared to the mean (FeatsBM) and mean(FeatsQM). The epoch is classified into the category where the selectedfeatures of that epoch are closest. The prediction model takessignificantly less computing resources than training the predictionmodel.

The performance of this pipeline is determined by looking at the errorrate of predictions of each epoch in the Test data into either Busy Mindor Quiet mind. We know the true Category since the User was asked to doan exercise that evoked a Busy Mind and then a Quiet Mind.

The parameters are varied and the above steps are repeated until theerror rate converges. So the parameters that minimized error rate werediscovered to be IIR filter bands 3 Hz, 6 Hz, 32 Hz and 36 Hz. OptimalEpoch length was 2 seconds. In addition to varying parameters, the RulesEngine may also specify variations in the steps taken. For instance,instead of an IIR filter use a Finite Impulse Response Filter, and orinstead of CSP use Independent Components Analysis to extract featuresfrom the signal.

The user mobile device may be loaded with the new parameters for thispipeline. The user can enjoy new neurofeedback guided sessions i.e.feedback in real time as to the strength (i.e. distance of theircalculated features to the training data they recorded during thecalibration session).

Example: Quantization of Numerical Measurements Feature ExtractionMethod

There are situations where it would be valuable to consider otherobservations like nominal features such as gender or hair colour alongwith numerical values to build Predictive Models. The approach describedbelow is to quantize the numerical values into discrete bins and thenuse Classifiers that operate on discrete values such as Naive Bayes,Decision Tree or Pattern Discovery.

The system platform receives an EEG signal that we can use to train orlearn a model. The EEG signal is divided into epochs across the entireEEG signal at every 10 ms. Other time slices can be chosen. The systemplatform calculates the median voltage within every slice across theentire EEG signal. The system platform can also take the average orother statistics across the voltages in a time slice. The systemplatform sorts the median values from low to high. For example, assume asignal is provided that is 100 seconds long yielding 10,000 slices. Thesystem platform may decide how many bins to use (such as #bins=5). Thesystem platform may divide the ordered 10,000 values into 5 parts,divided into bins having boundaries every 2000 voltage value.

The table of FIG. 13 shows an example of the bin boundaries used forquantization. These bin boundaries can be used to quantize other EEGsignals. A EEG signal time slice with median value 5 will have bin value3, and another time slice with a median voltage of −90.3 is in bin 1.Voltage values outside the lower boundary of bin 1 or upper boundary ofbin 5 are tagged to have special treatment and may be ignored or treatedas a gap in the signal that is filled by using interpolation.

Example: Pipeline Improver Using Naive Bayes Example

Naive Bayes has the advantage of being transparent in that other methodsuse complicated mathematical transformations that obscure what ishappening to the signals and make it difficult to interpret what ishappening. Naive Bayes can also be used with nominal variables combinedwith numerical variables. It can also calculate a probability for eachprediction it makes so that we get a sense of the confidence it has init predictions.

Assume training data comprises: twenty EEG signals of 3 minute recordingfor Category 1=“Busy Mind” and a separate 3 minute recording of Category2=“Quiet Mind”.

Assume test data comprises: twenty EEG signals of 1 minute recording forCategory 1=“Busy Mind” and a separate 1 minute recording of Category2=“Quiet Mind”.

Therefore, pre-processing of training data may comprise: an infiniteimpulse response bandpass filter, such as Butterworth Filter, startswith the default settings of 1-2 Hz for the lower stopband, 2 to 28 Hzbeing the passband, and 28 to 32 Hz for the upper stopband. At thestopband frequencies of 1 and 32 Hz the signal is attenuated by −50 dB.The ripple of 0.5 dB above the average passband gain occurs at 2 and 28Hz.

Feature Extraction of the Training Data may comprise the followingsteps. Each epoch is further divided into time slices of 20 ms inlength. The median value of each time slice is calculated. Ten bins arechosen for quantization. The median values across both the Quiet Mindand Busy Mind training data are sorted in ascending order to find theten bin boundaries as previously described.

Feature Selection may comprise the following steps. An exhaustive searchwill be conducted across all permutations of the twenty features (i.e.each feature corresponds to the quantized voltage value of an EEG timeslice per electrode). All permutations of single features, areconsidered, as are permutations of two features, then permutations ofthree features, and so on, until all twenty are considered. This is avery large search space that can benefit from parallel processing. NaiveBayes is the classifier that will be used. Naive Bayes calculates theprobability that a set of features belongs to a category using theprobability of each feature value occurring by counting the number ofoccurrences in the training data. The Naïve Bayes formula is shown inFIG. 14. In this example there are 9000 time slices in the Busy Mind and9000 times slices in Quiet Mind training data per channel. The totalnumber of occurrences in the training data are counted per bin perchannel and then divided by the total number of time slices to determinethe conditional probability of that feature bin given the session perEEG channel. The table of FIG. 15 shows an example count andcorresponding conditional probability for 1 Channel. A time slice thathas feature bin=8 has a 0.077 probability of belonging to Busy Mind and0.1 probability of belong to Quiet mind. The probabilities arecalculated per channel in this way for the training data for all featurebins. An exhaustive search is conducted by the pipeline improverexhaustively searching across all feature permutations and keeping trackof classification accuracy. The set of features with the highestclassification accuracy are chosen to be used in the pipeline for thisapplication. These new features are transmitted to the client device toupdate the user profile used by this application.

Parallel Methods for Cluster/Grid Computing for Pipeline Improver

The pipeline improvement Process is computationally intensive. Thepipeline improver has been designed such that it can be largelyparallelized so that it can make use of the computational scalability ofcloud based cluster/grid computing architecture and databases such asApache Hadoop that support MapReduce, or other parallelization schemes.

Parallel Preprocessing and Feature Extraction

Parallelization of these components involve creating a population ofavailable pre-processors and a population of feature extractors as wellas defining combinations of these components to be tested together. Therules engine defines the learning problem and informs the FeatureExtractor Designer of the prior knowledge of what Pre-Processing andFeature extractors to select from and the ranges of tunable parametersfor the selected methods.

Time contiguous data is extracted from a hypertable, and Preprocessingis mapped to be computed in parallel in a reduce operation. Preprocesseddata segments can then be mapped across feature computation workers. Thevarious feature types computer from the data is collated andredistributed to feature selection and classifier design (machinelearning) processes.

Parallel Feature Selection and Supervised Machine Learning ClassifierTraining

Feature selection is typically done using a wrapper method wheredifferent permutations of feature selections are used to train anon-linear classifier (such as a support vector machine with anon-linear kernel). One embodiment uses a genetic algorithm to permutethe features, and the resulting classification accuracy of the trainedclassifier is used to judge the fitness of each selection. Fitness isthen being used as part of the selection process for future generations,as is typical for genetic algorithms. Parallel forms of a geneticalgorithm are also used to distribute the computational load across acluster or grid of the system platform. Constraints relating to thereal-time computational resources are introduced into to the featureselection process to ensure that the resulting real-time estimators arerunnable on the target platform. Parameters will be platform dependant(e.g. an application running on an iphone3 will have a differentconstraints than and iphone4 which will have different characteristicsthan a desktop computer).

Feature selection using a parallel genetic algorithm may be implementedon Apache Hadoop, or on other platforms, and may utilize user definedMAP, REDUCE, and PARTITIONER functions.

Genetic algorithms can be parallelize in different ways such as: (a)global single population master-slave GA; (b) single populationfine-grained GA; and (c) multiple-deme GA (“island”).

An exemplary non-limiting implementation off feature selection maycomprise the following steps:

-   -   A: Initialize the population with random feature selection        vectors.        -   1. For mobile pipeline development, check that selected            feature combinations are computationally feasible on the            target device.            -   1.1 For calculation speedup,                -   use MapReduce to create the individuals; and                -   utilize prior probabilities based on prior pipeline                    development.    -   B: MAP: For each feature selection vector.        -   1. train a classifier (or set of classifiers to evaluate            different types or parameter spaces) where training of            different classifiers is done by a set of parallel worker            functions, as they are independent (e.g. soft margin support            vector machine (SVM) with Gaussian kernel);        -   1.2 evaluate the fitness based on detection accuracy using            cross validation;        -   1.3 return (feature selection vector, fitness); and        -   1.4 keep track of best (fittest) feature selection vector,            returning best result when all individuals have been            processed.    -   C: PARTITIONER: shuffle stage between map and reduce (randomly        shuffle individuals across reducers).    -   D: REDUCE:        -   collect small subgroups selection (e.g. Tournament            selection):            -   choose k (the tournament size) individuals from the                population using random sequence from the Partitioner;                -   choose the best individual from pool/tournament with                    probability p;                -   choose the second best individual with probability                    p*(1−p); and                -   choose the third best individual with probability                    p*((1−p)̂2);        -   collect a group of tournament winners and do crossover (E.g.            with half uniform operator):            -   select two sequential tournament winners, and evaluate                each bit in the winners feature selection vector, and                exchange with a probability of 0.5 (with 0.5 mixing                ratio, the offspring has approximately half of the genes                from first parent and the other half from second                parent); and            -   check to see if feature selection violates compute                complexity limit for the client device.        -   If complexity is okay, return the new feature selection            vector into the new test population.        -   If complexity is too large, redo crossover operation until a            computationally feasible solutions is selected.    -   E: REPEAT STEPS: A->D UNTIL CONVERGENCE CRITERIA IS MET.

FIG. 16 shows how the client pipeline may be updated.

Security/Privacy

In order to protect the user's privacy, a user may be required to grantaccess rights to certain data for certain applications. In an exampleembodiment, the client device 108 may allow a user to set privacypreferences 402 related to user profile data 114. These privacypreferences may then be used to control the data available to thirdparty applications or the SAAS platform 302. In the case of availabilityto third party applications, the client device may have for example anaccess controller 404 configured to control user profile data 112 accessbased on privacy settings 402. A skilled person would understand thatdifferent techniques for controlling access to resources may likewise beemployed. The privacy preferences 402 may provide for anonymization ofthe user profile data 114 when sharing the user profile data 114 withanother user or system.

Personal data security may be provided through encryption. Sharing ofun-curated data (giving people time-limited and fidelity limited access)though the sharing of encryption keys may be provided.

Privacy may be established between users that have a relationship;between users and researchers; and how this privacy is distributed inthe devices and in the Cloud.

When sharing biosignal data with others, privacy measures are defined.Sharing will not share how these measures are derived from each person.For example, where there is a measurement of happiness/sadness, thescore may be shared without absolute accuracy. Since sharing of howscores are computed is not done, there is less security risk of personalinformation.

The server which may be considered as trusted will be able to do moresophisticated correlational analysis of sensitive information, and willnot require that the sensitive information is transferred to a untrustedparty. The sophisticated methods of EEG and related analysis aresufficient to ensure that the system cannot be abused to retrievesensitive information through the joint analysis of bogus data andsensitive user data that is under attack.

The timing features of the signal may be shared rather than the signalitself. This may be like doing phase correlation. The privacy measuresprovide that it cannot reconstructed but carries timing information.Methods of encryption can be used where the keys vary over time. Theuser's application may use user passkeys in an algorithm to producepseudo random varying keys that will take time and date as inputs. Thiswill allow the user to give a third party access only to a particulartime range of data.

Encoding methods can also be used that cause separate encryption onmultiple levels of fidelity. Partial keys may be given to access aparticular level of fidelity for a time range, such that the user canfinely control how much information is released.

Privacy methods may include: biosignal+other data captured (utilizingencryption and encoding to protect transmission between the sensorhardware and the platform); timestamping of data (relative to a globaltime); preprocessing (data may not be immediately transmitted to cloud);and obfuscating of user detail by enumerating locations but notproviding a map (for faces; speaker identification; speech conservation;localizability geo location); and manipulating biosignal fidelity. Audioor video data may be transformed into intensity data or pre-filtering orcompressed using a lossy method. Change detection may be employed.Features are transmitted back such that adequate data mining can bedone, but full reconstruction of source data may be impossible. (e.g.does not constitute an audio recording, or video recording, and thusdoes not raise privacy concerns). The user could opt for full fidelityso that improved analysis can be done, like word association or speakeridentification, to correlate with cognitive state changes. Changedetection can be used for performing time-locked analysis of the data,such as ERP.

Other privacy methods may include: data is transmitted to the clouddatabase where personal data is encrypted; and data is accessed byanother party only when allowed by the user's security rules.

A limited time range of access can give others limited access to data asdetermined by their relationship to the user but also to when the useris allowed access. For example, a user's personal trainer may beauthorized by the user to receive full access to certain bio-signalswhen training, but not at other times. Time limitation can also help inprivacy protection in the sharing of data that has had very little postprocessing. Time-limited and fidelity limited access may be enabledthrough the sharing of encryption keys. The encryption keys can varyover time. The user's application will use user passkeys in an algorithmto produce pseudo random varying keys that will take time and date asinputs. This will allow the user to give a third party access only to aparticular time range of data. Encoding methods can also be used thatcause separate encryption on multiple levels of fidelity. Partial keysmay be given to access a particular level of fidelity for a time rangesuch that the user can finely control how much information is released.

With regards to signal fidelity, the system platform may maskuser-identifiable indicators, or use only class estimates. When sharingbiosignal data with others, measures are defined, but the systemplatform may not share how these measures are derived from each person.For example, where there is a happiness measurement, the system platformmay share the score but have no absolute accuracy. As there is nosharing of how the scores are computed, there is less of a security riskof sharing personal information. Use-only features such as eventinformation may be shared instead of user-identifiable features. Thesystem platform may only provide for sharing of timing features of thesignal rather than the signal itself. This process of anonymization maybe similar to a process of phase correlation. A recipient could notreconstruct the signal but the signal carries timing information.Examples of data that may be shared: audio features that mark whenpeople start and stop talking; EEG features that mark when the userexhibits an ERP or ERN; and heart rate (“HR”) features that marksignificant increases or decreases in HR.

Physical and mental state classification and activity classification maybe implemented by the system platform. A list of rules may be maintainedidentifying which users have access to what classes, and at whatresolution. Security encompasses class specificity and temporallocalizability.

There may be blurring of classes since some classes might be consideredmore private than others. Some activities may be considered by the userto be shareable, while others are not considered to be shareable (e.g.doctor visits or therapy sessions). A mental state estimate may beshareable but not the context. Instead of sharing a high granularity ofaffective state, the system platform may be configured to share that isnegative or positive neutral energy. The shared measurement may be tooheavily quantized to yield a definitive indication of happiness orsadness.

Temporal aspects of class estimates can be blurred (e.g. provide averagemood for the last day rather than a minute by minute account). The usermay allow detailed analysis to certain individuals, or perhaps tocertain individuals when the user is located in close proximity to thecertain individual.

Often the purpose of sharing data is to do correlational analysis orother data mining operation over an individual's (or group's) data withrespect to another data set of interest. For example, if you wanted tofind out how many people were happy all at the same time during theexperience of listening to a specific song. The system platform whichmay be considered as trusted will be able to do more sophisticatedcorrelational analysis of sensitive information and may not require thatit is transferred to a untrusted party. The sophisticated methods of EEGand related analysis are sufficient to ensure that the system platformcannot be abused to retrieve sensitive information through the jointanalysis of bogus data and sensitive user data that is under attack. Forexample, a song in question could be uploaded to the server. The systemplatform could discover what users have listened to the song, and asecondary operation could collate the responses and report backanonymous statistics that respect privacy settings of individual users.

Sensor Types

Many sensor types may be used to obtain data from the user or regardingother properties or events, including, but not necessarily limited tothe following: 3 axis Accelerometers; Acoustic; Air velocity; Gasdetectors; humidity sensors; weight scales; infrared camera; magneticfields; pH level; Speed; atmospheric pressure; Blood glucose; Arterialblood pressure; Electrocardiography; Temperature; Respiratory rate;Pulse oximetry; environmental (e.g. thermometer); brain activitymeasurement devices (e.g. EEG for FNIRS); cardiovascular activitythrough ECG or pulse oxymetry; muscle activity using EMG; breathmeasurement using strain sensors; skin conductance; body motion usinginertial sensors such as gyroscope and accelerometer; environmentalsound (microphone); environmental visuals (video and images); mediaplayers; geographical based context; tasked based context; telephonecalls; email reading and composition; and internet searching andreading.

Bio-signal data may include heart rate or blood pressure, whilenon-bio-signal data may include time, GPS location, barometric pressure,acceleration forces, ambient light, sound, and other data. Bio-signaland non-bio-signal data can be captured by the internal sensors 106,external sensors 104, or both.

Sharing Data

Referring now to FIG. 3, in a non-limiting exemplary embodiment, theclient device or devices may have an interface for sharing, with thirdparty applications 402, user profile data 114 or system functionality.This interface may include, for example, access to sensors and softwareused to process and analyze data. This interface, for example, could bean application programming interface (API) 401 that third partydevelopers may use to access data and functionality associated with aclient device. API 401 may provide access to the SAAS platform 200through the client device API.

In order to protect the user's privacy, a user may be required to grantaccess rights to certain data for certain applications. In an exampleembodiment, the client device 108 may allow a user to set privacypreferences 402 related to user profile data 114. These privacypreferences may then be used to control the data available to thirdparty applications or the SAAS platform 302. In the case of availabilityto third party applications, the client device may have an accesscontroller 404 configured to control user profile data 112 access basedon privacy settings 402. Other techniques for controlling access toresources may likewise be employed.

In another broad aspect, a method is provided for collecting bio-signaland non-bio-signal data from a user, processing the bio-signal andnon-bio-signal data, analyzing the bio-signal and non-bio-signal data,and sharing the analyzed bio-signal and non-bio-signal data with thirdparty applications, clients, and organizations.

Possible MED-GASP system features of the present invention include:Uploading brainwaves & associated sensor and application state data tocloud from mobile application; Downloading brainwave & associated datafrom cloud; Real-time brain-state classification to enable BCI in gamesor other applications; transmitting real-time brain-state data to otherusers when playing a game to enable multi-user games; Sharing brainwavedata with other users to enable asynchronous comparisons of results;Sharing brainwave data to other organizations, third party applications,and systems; sharing brainwave data with other applications on mobiledevices through a cloud services web interface; and Sharing brainwavedata with other applications running on client devices or other devicesin the trusted network such that brainwave data can control and/oraffect other devices.

The system platform may provide the ability to use pre-compiledalgorithms supplied by a third party within the system platform library,including the ability for a third party who is using the system platformlibrary, to use the third party's own developed algorithms with thesystem platform library. The system platform may allow users to buildand participate in communities of like-minded meditators, for example byenabling real time coaching from a remotely located meditation coachusing the system of the present invention.

In one implementation, based on permission by a first user, a seconduser may be notified when the first user is using the meditationapplication, and may enable the second user to access a dashboard thatenables in real time or near real time to track the progress of thefirst user. The system platform may enable the second user to provideencouraging guidance or messaging to the first user for example byproviding a voice link or to enable the integration of messages from thesecond user into an interface presented by the client application of thefirst user.

The present system may include a social media layer that allows users to“friend” one another based on shared attributes such as similarinterests, similar profiles (including for example similar pace orchallenges in achieving meditative states for example or differences inprofiles that suggest that a first user may be able to assist a seconduser in achieving defined objectives). The social media layer may enablea variety of social interactions between users, including for examplesupport provided by one user to one or more other users in achievinggoals of applications. Support may be expressed by sending supportivemessages, encouraging digital media objects such as badges and so on.Various other implementations are possible.

The present invention may give feedback to the user. This may beaccomplished using the latest research in the EEG/meditation field toprovide to the user the most accurate measures known to facilitate theuser's learning. A user may be assisted in meditation by allowing theuser to observe the user's progress and provide motivational support forthe user's meditation practice.

In one implementation, a user wears a brainwave headset that isconnected to the user's mobile phone while the user's meditates. Anapplication on the phone processes the brainwaves and provides feedbackabout the user's brain state using neurofeedback to help the userachieve deeply meditative states and to speed the user's learning ofmeditation states. On the mobile device, the user's data is recorded andwill be used to generate a personal meditation history that shows theuser's progress over time.

The present invention may provide for integration of pre-meditation andpost-meditation collection of subjective experience factors (such as theuser's mood and level of alertness to support more comprehensive resultsto the user and provide features to be used in the server side algorithmimprovement engine). The user may therefore be able to track qualitativeaspects of the user's practice and see trends that can improvemotivation and speed. Survey answers also allow the meditationexperience to be tailored to suit user preferences.

Program tools to download brainwave and associated data from the cloudmay be used and conversion into MATLAB™ and PYTHON™ data formats may beprovided.

Integrating audio feedback may allow the user to know when headsetsignal quality is poor, or when other program functionality is impaired.

Implementing a meditation timer with bell stimulus that can be timelocked to EEG analysis may support stimulus entrainment ERP analysis.

Pod™ player integration (or integration with other wireless devices) maybe provided, to synchronize music with EEG collection to support timelocked stimulation and analysis relating to music. Synchronizing musicin this way is related to measuring meditation performance as well asimplementing a music recommendation system.

Incorporating an attentional blink cognitive test for meditationprogress scoring may be provided.

Use of acoustic entrainment based meditation performance measures may beprovided.

Multi-stable perception analysis, using visual illusions such as theNecker cube, Schroeder staircase and Rubin's vase, to measure EEGdynamics related to cognitive re-framing may be provided.

Other features may include: choose your own adventure-style decisiontree for meditation education and presentation sequence, constructivefeedback and encouragement; environmentizer for voice in guidedmeditation; Mr. Potato head-style body scans; Chest and head breathmovement analysis; Hands fidget analysis; hands breath counting; phaselocked loop rhythmic entrainment (i.e. rocking of body); rhythmicbreath; rhythmic alpha and or theta; and Paired Synchronizationmeditation; and standing balance meditation and yogic poses before andduring meditation.

An augmented reality meditation environment may be provided where visualworld changes when in different phases of meditation.

SAAS

Any functions performed by the client device or devices could also beperformed by the SAAS platform 200. Raw data collected from internal andexternal sensors may be sent directly to a SAAS platform for processing,analysis, and storage. However, bandwidth limitations, may require thatat least some of the processing and analysis is performed on the clientdevice(s) prior to transmission to the SAAS platform 200.

The SAAS platform 200 may also be used to perform computationallycomplicated or expensive tasks that may not be performed as efficientlyor effectively on the client device or devices. Furthermore, the SAASplatform 200 may be used to store user data for many users. Since it canstore data for many different users, the SAAS platform 200 may alsoanalyze data associated with one or more users. This aggregate dataanalysis may be used, for example, to determine recurring patterns forvarious groups of users.

In an example embodiment, the SAAS platform 200 may comprise acloud-based database 202, an analyzer 204, an algorithm collector 206,and an algorithm improver 208. The cloud based database may store all,or a subset, of the raw data 112 associated with a user profile 114. Thedatabase may also store user data that has been pre-processed oranalyzed by the client device or devices. Alternatives to cloud-baseddatabases could be used.

Similar to the client device or devices, the analyzer 204 in the SAASplatform 200 may be used to process and analyze raw data collected byinternal and external sensors. Additionally, the analyzer 204 may beconfigured to utilize algorithms stored in the algorithm collector 206to process and analyze both raw data and user profile data.

In addition to analyzing data associated with individual users, theanalyzer may analyze aggregated data (data associated with one or moreusers). Aggregate data analysis, may provide insight into brainwavepatterns for a particular population. An analysis over a set of usersmay indicate, that one or more patterns of features as extracted by thefeature extractors discussed earlier are associated with a specificmental state for certain types of users.

In one example embodiment, the analyzer 204 may utilize a machinelearning algorithm stored in the algorithm collector 206 to identifyand, correlate patterns with specific types of users. These algorithms,for example, may identify patterns common to many users.

More than one machine learning algorithm can be stored in the algorithmcollector 206 and multiple algorithms may be supplied to the analyzer204 depending on the circumstances. Another embodiment might allow thirdparty applications or other parties to supply their own customalgorithms into the algorithm collector 206 so that the custom algorithmmay be used by the analyzer 204.

The analysis results can be used by an algorithm improver 208 to improvethe algorithm for future analysis. For example, analysis of the data mayuncover different brainwave patterns for different classes of users thatmay correlate with specific physical or emotional states. These patternscan then be used to update the algorithms so that these patterns areused in the analysis of data collected in the future. There may becorrelations found between specific groups of users with a specific setof characteristics. These correlations may be used to improve theperformance of the analyzer 204, algorithm collector 206, or algorithmimprover 208, or other aspects of the systems or methods.

Improving the algorithms may also comprise a feedback mechanism to allowusers of the system to judge the correctness of the analysis. That is, afeedback process may be used to allow the user to evaluate or supervisethe machine learning. This feedback mechanism, in an example embodiment,can be a part of the algorithm improver 208. For example, a user of theclient device may be asked whether a particular analysis correspondswith a particular emotion or physical movement experienced at the timethe bio-signal and non-bio-signal data was collected. This can be usefulwhere there is more than one pattern for a known physical or emotionalstate. The feedback mechanism may allow supervisors to review theaggregate data analysis for correctness. The feedback mechanism mayallow supervisors to tune the aggregate data analysis algorithm toidentify and ignore aberrant data.

The algorithm improver 208 may be used to re-analyze the data. Thisre-analysis can both validate and improve the analysis algorithms. Thealgorithm improver 208 may first update the analysis algorithm in thealgorithm collector 206. The data may then be re-analyzed by theanalyzer 204 using the updated algorithm stored in the algorithmcollector 206. Also, the algorithms 116 on the client device or devicesmay be updated over the connection 301 by the SAAS platform 200. Thealgorithms may be improved through machine learning applied to collecteddata either on-board the client device or on the server. EEG data may besaved along with application state to allow a machine learning algorithmto optimize the methods that transform the user's brainwaves into usablecontrol signals.

In another embodiment, the SAAS platform comprises an interface forsharing stored and analyzed user data to other client devices,organizations, or third party applications. As can been seen in FIGS. 1and 2 for example, examples of other client devices, organizations, orthird party applications 500 can include untrusted devices, the public,friends, trusted devices and third party applications. These devices,organizations, and applications can connect to the system via wired orwireless connections 301, for example over the internet. Furthermore,these connections may also be encrypted 302 using similar systems andmethods described above.

FIG. 4 shows an example embodiment of how the SAAS platform 200 might beimplemented. In FIG. 4, the mobile application 601 and web user 602 mayrepresent the client devices, organizations, or third party applications500 as discussed above. In this example, requests from theseapplications would first go to a load balancer 603, an example of whichwould be an HAProxy™. The proxy, based on the load of particular webservers would then route requests to one of at least one web applicationservers 604. These web application servers may be standalone servers, orthese web application servers might also be virtualized servers runningon hosting infrastructure such as Amazon's EC2™ platform or usingVMWARE™ technology. These web applications may also be implemented usinga variety of development languages or platforms. In one example of theimplementation of the present invention, the web application may beimplemented using Ruby on Rails™. These web applications may store userdata for example in a mySQL database 605. These web applications mayalso store data or detailed statistics on other storage systems, e.g.Hadoop. In this example, data analysis of large sets of data may beperformed by a cluster of machines configured to communicate with thedata store of the web application.

In accordance with aspects of the present invention, an implementationof the system platform is shown in FIGS. 18 and 19.

Other example aspects and embodiments of the systems and methods of thepresent invention are described below.

Meditation Application

According to an embodiment, systems and methods for a meditationbiofeedback application are provided. The embodiment comprises a mobilemeditation solution including an EEG headset bundled with an applicationthat measures a user's brainwaves while the user meditates and tracksthe user's progress over time. The product may be configured to befunctional out of the box and deliver particular benefits (reducedstress, improved mood, increased effectiveness in the workplace, etc.).The application may be implemented to a range of different platformssuch as iOS™ and Android™, and may help a user to learn to meditate. Theapplication may allow a user to observe the user's progress and providemotivational support for the user's meditation practice. The applicationmay allow a user to build and participate in communities of like-mindedmeditators, for example by enabling real time coaching from a remotelylocated meditation coach using the system of the present invention. Theapplication may use updated research in the EEG/meditation field toprovide the user the most up to date measures known to facilitate theuser's learning

In one implementation, based on permission by a first user, a seconduser may be notified when the first user is using the meditationapplication, and may enable the second user to access a dashboard thatenables in real time or near real time to track the progress of thefirst user. The system may enable the second user to provide encouragingguidance or messaging to the first user for example by providing a voicelink or to enable the integration of messages from the second user intoan interface presented by the client application of the first user.

In one implementation, a user wears a brainwave headset that isconnected to the user's mobile phone while the user meditates. Anapplication on the phone processes the brainwaves and gives the userfeedback about the user's brain state using neurofeedback to help theuser achieve deeply meditative states and to speed the user's learningof meditation states. On the mobile device, the user's data is recordedand will be used to generate a personal meditation history that showsthe user's progress over time.

The user's data can be processed and analyzed on the client device, orthe data can be uploaded to a cloud database for processing andanalysis. For example, the user's data is uploaded to a cloud databasewhere machine learning algorithms can process the data and therebycustomize the brainwave processing algorithms to better fit the user'sbrainwaves. The adaptations are then offered back to the user throughthe user's online profile, to enhance the user's experience.

Implementations of the application of the present invention may includeone or more features, including live group guided meditation, where theinstructor receives real-time information about the brain states of thesubjects. Subjects may also be able to receive updates from each otherincluding real-time brain state and other relevant measurements such asbreath phase.

Another feature may be integration of pre- and post-meditationcollection of subjective experience factors, such as the user's mood andlevel of alertness to support more comprehensive results to the user andprovide features to be used in the server side algorithm improvementengine. This may allow the user to track qualitative aspects of theuser's practice and see trends that can improve motivation and speedprogress. Survey answers also allow the meditation experience to betailored to suit user preferences. The present system includes a socialmedia layer that allows users to “friend” one another based on sharedattributes such as similar interests or similar profiles, including forexample similar pace or challenges in achieving meditative states forexample or differences in profiles that suggest that a first user may beable to assist a second user in achieving defined objectives. The socialmedia layer may enable a variety of social interactions between users,including for example support provided by one user to one or more otherusers in achieving goals of applications. Support may be expressed forexample by sending supportive messages, encouraging digital mediaobjects such as badges and so on. Various other implementations arepossible.

Another feature may be cloud based extensible user profiles for personalinformation; settings and algorithm parameters that are continuouslytuned (through the use of the application) to be improved or optimizedfor the specific user; hardware setups and application specificparameters; ad relevant training data. This may allow application usageto be device independent, and allow for back compatibility as theapplication is improved.

Other features may include: augmented reality meditation environmentwhere visual world changes when in different phases of meditation;program tools to download brainwave and associated data from the cloudand convert into MATLAB™ and PYTHON™ data formats may be provided;integrated audio feedback to allow the user to know when headset signalquality is poor, or when other program functionality is impaired;meditation timer with bell stimulus that can be time locked to EEGanalysis to support stimulus entrainment ERP analysis; IPod™ playerintegration (or integration with other wireless devices), to synchronizemusic with EEG collection to support time locked stimulation andanalysis relating to music (this may be related to measuring meditationperformance as well as a music recommendation system); an attentionalblink cognitive test for meditation progress scoring; use of acousticentrainment based meditation performance measures; multi-stableperception analysis, using visual illusions such as the Necker cube,Schroeder staircase and Rubin's vase, to measure EEG dynamics related tocognitive re-framing; choose your own adventure style decision tree formeditation education and presentation sequence, constructive feedbackand encouragement; environmentizer for voice in guided meditation; Mr.Potato head body scans; Chest and head breath movement analysis; handsfidget analysis, hands breath counting; phase locked loop rhythmicentrainment, such as rocking of body, rhythmic breath, rhythmic alphaand or theta, and/or Paired Synchronization meditation; and standingbalance meditation and yogic poses before and during meditation.

Compare-Your-Brainwaves

In one possible extension of the present invention, each user wears abrainwave headset connected computing device. A sample of each user'sbrainwaves is taken, while resting or while performing a certainactivity. These brainwaves are then uploaded to the cloud where a cloudimplemented resource implements one or more processing algorithms forrating the brainwave samples according to several criteria. The resultsof this analysis may be compared with that of other users, eitherfriends from a social network for example, random users from the publicor celebrities, who collected their brainwave data in a similar test.The comparison data is then sent back to the client application on themobile device, where users can see how similar they are to one another.Music experience vectors (“MEV”) can be stored in the cloud that listthe music and the brainwave analysis results that lead to the musicrating.

In another possible related implementation, music recommendation enginebased on brainwaves may be provided. In this case time-locked brainwavesmay be recorded as a user listens to a particular piece of music. Thisbrain state data is reduced to a lower dimensional MEV that encodes timelocked attentional entrainment. In one aspect, the music recommendationengine may compare the user's MEV with MEVs of other users listening tothe same song to identify other users that have high MEV correlation.Because of the time-locking between the MEV and the piece of music thatwas listened to, users that have highly correlated MEVs are likely torespond to similar parts of the song in question, and are thus likely toexperience other songs similarly as well. This music recommendationengine can then create a set of recommended songs that are new to theuser that are liked by other users with similar MEVs, or it may suggesta social connection with other users of similar taste. Therecommendation could incorporate many MEVs in its user matchingprocesses.

Quantified Self Integration

The present system may be integrated with other “quantified self”products such as the Jawbone UP, Fitbit, BodyMedia, heart-rate monitorsand the like. For example, data from these third party systems may beobtained and synchronized with data created by the present system, forexample its MED-CASP system implementation as described. In oneimplementation, the system of the present invention may transformambiguous intangible brainwave data to clear tangible information whichis then processed and transformed into info-graphics and self-metricsserving as a tool to measure and increase an individual's human capital.The system would be designed for use in everyday life, recording auser's brain-state over the course of an entire workday. At the end ofthe day, the user would download that day's data into a computer, whichwould analyze it and compare it with previous data looking for patterns.The computer would prompt the user to enter details about the day'sactivities, and then correlate them with the user's brain states.

Epilepsy Monitor that Lets Your Doctor See Raw Data

In another possible implementation of the present invention, an improvedepilepsy monitor system may be provided. In this aspect, an epilepticpatient wears an EEG headset that records the patient's brainwavesthroughout the day and streams the brainwaves to a client application ona mobile device. In the event of a seizure, the application may notifyautomatically the patient's doctor, contact emergency services viaInternet connection, and/or provide context information such as GPS andauditory environment, and brainwave history pre and seizure as well asreal-time EEG data.

EEG+Augmented Reality

Use of visual feedback for neurofeedback may be desirable however it isnot necessarily compatible with real world applications since the useris typically using the user's vision to achieve a primary task andhaving to look at a screen disrupts this. Augmented reality (“AR”)generally refers to technologies where the users field of view (“FOV”)is augmented, or otherwise changed, by computer graphics. AR would allowa user to receive biofeedback naturally, in a way that that can beintegrated into many different experience and applications. In oneimplementation, an augmented reality system may be provided thatincludes or links to the system platform of the present invention. Theaugment reality system may include a wearable display (often called aheads up display). Possible features of an augmented reality systembased on the present invention may include: identification of brainstates, specific to the AR context, that utilize context informationfrom the inertial tracking and the computer vision processing donewithin the partner companies' aspect of the application. This mayinclude, for instance, the identification of P300 waveforms time lockedto visual events in the AR field of view.

A system platform employing ERP or other brainwave features may be ableto determine if there are visual events in the users FOV that aresignificant. A surprising or significant occurrence may be observed,including, for example, seeing a car that the user likes, or witnessinga threatening event that could cause the computer system to go intoquiet mode and allow the user to concentrate more on the user'senvironment rather than the computer world. The system platform may beconfigured to record the faces of other people that the user recognizesor wants to remember. The system platform could maintain a database fora new individual that the person has met, or will meet. The systemplatform may take pictures or start recording video if the visualinformation is salient or interesting to the user.

Many applications can also be built that leverage the features of thetechnology directly. For example, an application may be built thatallows a user to see another person's brain state rendered in real timeemo-graphics (i.e. perhaps around the other person's head or body) toenhance social interactions over lower than real life bandwidthchannels, or to enhance interactions between strangers or between peoplewho speak different languages.

For example, when the user looks at a person with whom the user isconversing, people who are upset could have storm clouds rendered abovetheir heads, or people who are happy could have sunshine streaming fromthem. People who are thinking could have the gears turning renderedabove them with computer graphics. People who are relating socially toothers in their proximity could show lines of interaction betweenthemselves and the people with whom they are relating as lines ofcoherence between people. It would be useful for some interactions toshow the coherence between the users brainwaves and another, both thepeople who the user is communicating with or perhaps even those withwhom they are relating but without direct verbal contact. Accordingly,such an implementation may help users to synchronize which mayultimately affect the quality of communication and the feeling ofconnectedness between people.

EEG Capture System for Visual Salience Monitoring for Video Summarizing,or Image/Video Tagging.

In another possible implementation, photos or videos that are capturedby an operator using a MED-CASP system may be tagged with brain statefor searching and annotating by/with affective state. Videos may be autosummarized at different scales based on TF analysis, visual or auditoryERPs or other EEG processing technique.

For example, output of a digital camera or video camera may be taggedwith brain state information. Augmented reality devices that incorporatea camera (like Google Glass) may be configured to record video (of whatthe user sees) along with brain state information, or, for example,“find me all the videos I took where I was really happy, or in Flow”.Tagged video may be searched for highlights of the user's day where“highlight” is determined through affective/cognitive state asdetermined using brainwaves and other biometric information. The systemplatform or connected AR device may display the times in the day wherethe user was distressed, and may therefore allow the user to start tounderstand the user's emotional triggers and relationships.

Software Development Kit (SDK)

A software development kit may be provided which allows third parties todevelop applications using the system platform product. The SDK mayprovide third parties: to access the raw brainwave data; plug in newalgorithms for DSP processing; plug in new algorithms for theinterpretation of the DSP data; and display monitoring data as to thecurrent state of the headset, FFT algorithms, DSP algorithms, andapplication-level algorithm data.

Team Based Applications

In a non-limiting exemplary implementation of the present invention,information about teams and results can be stored in the User's Profile.A team (group of users with a headset device) can be formed around acommon goal or principles. Teams could, for example, participate incontests, collect awards, and participate in simultaneous live eventswhere groups compete or collaborate with each other. For example, in oneapplication, a team may collectively collect points toward a goal. Teamshave common properties such as a team name, description, list of teammembers, apps they participate in, individual rankings for each app, andoverall team standing versus other teams. A facility may be provided forindividuals to search for teams and for teams to advertise themselves. Asingle team may participate in multiple applications. An application maybe participating simultaneously or asynchronously while being eitherdisconnected from the Internet or connected through the respective teammember's mobile device. A disconnected app could be operating on a localwired or wireless network. A team member can view the collective datafor a team as well as view information about other participants.

Shared Experiences LED by a Facilitator

Sessions and results can be stored in the user's profile. Participantsjoin a “room” which connect participants together. One of thoseparticipants is a facilitator or “group leader” (GL) who guides thegroup on a shared experience. To aid the group leader in his/her task, adashboard is displayed on the GL's device which allows the monitoringand/or communication with each participant. The group leader can sendmessages to the participants (such as text, voice, headset data) eitherpublicly or privately. The GL can create or delete rooms, allowparticipants into the room, or remove them from the room, or set rulesfor the room. Room rules may include: anyone can join; need a securitytoken to join; whether the session is being recorded; allow/denyuser-to-user communication; allow/deny public broadcasts messages fromparticipants; allow/deny participants to see identifying informationabout other participants; and provide one or more participants withpermissions to manage the room and/or other participants. The groupleader can be connected to more than one room at a time. Eachparticipant may be able to monitor and/or communicate with the groupleader. A participant can be connected to more than one room at a time.A participant's client computing device: may upload live brainwave dataor previously recorded brainwave data; may choose to expose identifyinginformation to other participants; and may send private or broadcastmessages to other participants. This functionality may be implementedusing a server over the Internet, or other communications network, as arelay device, or the application could be run disconnected from theInternet over a local wired or wireless network. The server may be usedas an additional source of processing power to aid in the groupexperience, such as generating additional display data.

Sleep Monitoring and Aid Device

In another possible implementation, the system platform of the presentinvention may be adapted to provide an overall solution that adaptssleep algorithms to particular users. The system platform may offerunique functions, which may include: environmental control of music orlights (e.g. dimming the lights or turn off the lights while when theuser falls asleep); working with a meditation trainer application aspart of the sleep therapy; real-time sleep competitions with friends toincentivize users who typically stay awake late at night to go to sleepearlier; use the collected EEG data to create artistic representationsof the user's unconscious hours; and provide specialized algorithms tofacilitate dream states and lucid dreaming. Sleep results and analysiscan be stored in the user's profile

Social Applications

The system platform may provide for the ability to share a neurofeedbackvisual and auditory experience with others. This could be donesynchronously in realtime in a one to one fashion or in a broadcastfashion (one to many) or it could also be done in a multiway fashion(sending to many and receiving from many in a group). This could also bedone asynchronously, where the user can send or others can browse andchoose to view someone else's shared experience stored in the cloud.There are multiple network configurations for this type of socialengagement between users, and it could occur via direct peer to peerconnection, via local network or via the system cloud infrastructure.

The general architecture for this backend model may be similar to apublisher subscriber design where each node in the network (anindividual user) is both a publisher of content as well as a subscriberto other users or the cloud.

White Labeled Platform as a Service

A backend of the system platform may be designed in a modular fashionsuch that each module has configuration and meta parameters such asversioning so that it can be partitioned and subselected for release toa client as an individual platform. Meta parameters such as sharing orSDK level may allow to selectively share particular features andidentified algorithms with particular partners or retain theminternally. This has implications for a tiered SDK as well as tieredcloud server API.

Experiment, Research and Scientific Study Tool

Results learned in studies can be used to update a user's profile basedon the information in the user's profile. For example, the user'sprofile may be updated to state that, based on the user's brainwaves andother information the user is at a higher risk for developing disease X.

The cloud may support conducting medical, anthropological or socialstudies with the data contained in the cloud. The system platform may beused to design a study protocol and select study parameters. The studyprotocol and its parameters can include the definition of the populationto be studied (e.g. ADHD diagnosis compared to normal), and the type ofstudy to be conducted (e.g. randomized clinical trial (RCT), prospectiveor retrospective longitudinal cohort studies, cross sectional studies,pilot studies or other observational or clinical type of studies). Asoftware based controller in the cloud can then execute operations forquerying and selecting users, in a randomized fashion to belong todifferent study arms (e.g. sham versus neurofeedback). If the study isprospective or a comparison type study (e.g. RCT of different algorithmsor treatment effects) then instructions may be given to users specificto each arm of the study for them to follow. The controller can thenprovide alerts to users to ensure that they are complying with the studyprotocol. Then the controller can gather the data relevant to the study,anonymize any identifiers and build a data set that can be analyzedoffline by a human analyst or automatically online by study softwarebuilt-in the Cloud platform.

The backend cloud system may be designed to serve as an external andinternal research tool. For internal research the cloud system willserve as a driver for A/B testing with subsets of the user populationfor algorithm or application or user experience experiments. Thistesting infrastructure may also be useful for external clients orresearchers looking to run targeted experiments on specific cohortswithin the general population, such as a focused attention test for ADHDpatients in a given age range vs. a similarly aged control group. Anexample of this type of configuration is shown below in Figure GroupExperiments.

Affect Sensitive Cell Phones

In another possible example of use of the present invention, a mobilephone may be modified, and integrated with the MED-CASP systemimplementation of the present invention in order to (A) selectsring-tones and (B) scale ring volumes to match the user's emotionalstate and level of distraction. This would enable the system platform ofthe present invention to behave like a private secretary who is tunedinto the user's mood. The user profile can provide information to drivemood enabled apps.

Distraction Monitor

In another possible implementation of the present invention, thecomputer system of the present invention may be configured to act as adistraction monitor. This may be used for example to improve the socialaspects of telecommunication, or to communicate the cognitive state andlevel of distraction of the person that the user is talking with. Forinstance, this would allow for cognitive state feedback of a cell phoneuser that is driving. The system would be able to inform the other userof the relative balance of attention between the conversation andexternal factors where the parties to the conversation are remotelylocated. This type of information would normally be visibly communicatedbetween conversation participants located at the same location.

Affect Controlled Music Player

In another possible implementation, EEG based affect measurements may belinked with music libraries, and these measurements may be tagged formood transition probabilities of specific music. One implementation of acomputer system configured using the present invention may enable a userto choose a target brain state or mood, and the computer system mayautomatically generate a playlist of songs associated with that brainstate to facilitate a brain state transition or state reinforcement.

Radio DJ Feedback Machine

In another possible implementation of the present invention, computersystems may be provided that enable improvements to interactions with anaudience. For example EEG based audience music feedback may be used by aparticular implementation of the present invention for content controland selection. A listener a radio station (online or broadcast) may belistening using a mobile device enabled with functionality of thepresent invention, such as the MED-GASP implementation describedpreviously. Processed brain-state data may be sent to the cloud from themobile device, where it is shared with the DJ on the radio station. TheDJ may be able to view all of the data from these users and tounderstand how the users are responding to his or her musicalselections. From there, the DJ can adjust future selections or sharethat information with the audience.

Cloud-Based Movie Experience

In another related example, an entertainment system may be configured to(A) obtain statement of mind or mood information from an audience, and(B) adapt entertainment content (for example story ending) based on thisinformation in order to achieve maximum effect.

Student Attention Monitor

In another possible implementation of the present invention an educationmonitoring system may be provided where students in a classroom are eachwearing a brainwave headset which is monitoring their state ofattention. The teacher is able to access their brainwave data from adashboard at the front of the classroom, and can use it to monitorwhether students are concentrating on their work or not.

Guidance, Feedback and Teaching of Users

In accordance with a non-limiting implementation of the presentinvention, the user may choose a goal that the user wishes to reach byproviding an indication of the goal to the system platform. A guidancesystem component of the system platform in the cloud may then select ateaching program that is customized to the user's specific level for thegoal in mind. The guidance system may conduct a survey to help the userclarify the user's goals and then choose an EEG acquisition protocol tohelp characterize a patient. A decision tree is consulted that maps theuser's current state with respect to the goal and then recommends alearning program for the user. The learning program can include elementsof neurofeedback plus other exercises for the user to complete. Theguidance system can track a user's progress and provide encouragement,advice or alter the teaching program as required. The system uses EEGand other biological data to understand the user's emotional state andadjust the challenge of the learning paradigm according to the user'sskill level. For example, high challenge and low skill may lead toanxiety which decreases a person's productivity and reduces the person'sability to learn. On the other hand, low challenge and high skill levellead to boredom which usually means a person becomes disinterested incontinuing with the pursuit. A state of flow may be achieved where thechallenge placed on a person is matched to the person's skill levelproviding for the person to be productive with seemingly little effortand negative emotion attached to the effort. The guidance system maydetermine a user's emotion and then adjust the challenge placed on aperson so that the person is operating in a state of flow.

Monitoring Fatigue/Sleep in a Fleet of Truck Drivers

In this possible computer system implementation, truck drivers wearheadsets that record their level of fatigue. The data from theseheadsets is sent through the cloud to the dispatch where dispatchers canmonitor each driver and make sure that they are still able to drivesafely.

Quality Assurance in a Factory

In this possible computer system implementation, workers in a factorywear headsets that measure their level of attention while performingsensitive work or potentially dangerous work. If a worker's focus dropswhile working on a particular piece of equipment for example, thatequipment can then be marked to receive extra-attention in thequality-assurance process because of the increased likelihood of error.

Collective Brainwave Control

In other possible computer system implementations of the presentinvention, brainwaves may be obtained for a group of game players, andthese may be processed to enable collective control such as in acooperative multiplayer game. This type of interaction would be wellsuited to compassion training.

Brainwave Controlled Acoustic Attention Beam Forming.

In another possible computer system implementation the MED-CASP systemimplementation for example may incorporate a microphone array thatenables thought assisted beam-forming to allow a user to enhance auser's ability to lock into directional sound in a noisy setting. Thealgorithm may for example associate dynamic changes in brain state withthe persons desire to “tune in” to different aspects of the soundscape.

Brainwave Focus Disruptor/Encourager.

In another possible implementation of the present invention, the systemplatform may be configured to enable improved cognitive skills trainingfor example using cognitive performance training, attention masteringand distraction avoidance. The system platform may be configured toaffect the informatic channel between the user and their environment, toeither help the user focus the user's awareness or to break free fromsomething that is commanding the user's attention in a detrimental way.This includes for instance an EEG based information filter for webbrowsing or other computer use. For instance the system platform couldhelp to make distracting advertisements or messaging disappear when theuser is trying to work and are having trouble concentrating. Detectionfor such distractions or interruptions may be performed through thetaband phase locking for visual and auditory stimulus. The system platformmay also detect fatigue related zoning and break the user from mindtraps such as video games and web surfing. These concepts can beultimately applied to the augmented or mediated reality applicationswhere the audio visual signals are first intercepted by a computersystem before the user receives them and allows for intelligentintervention that can help a user filter out distracting informationfrom the user's environment, so that the user can be less confused oroverloaded.

Medical Applications

EEG signals along with other biological and non-biological data can beused to diagnose medical and or psychological disorders. Rules used fordiagnosing can be learned from the data stored in the system platform.These rules can also be obtained from clinical practice guidelines orother evidence based medical literature.

ADHD—Diagnosis

Analysis of EEG signals can be used to classify a person as exhibitingattention deficit hyperactivity disorder (“ADHD”) or not. A user mayundergo a prescribed set of exercises while the user's EEG signals arerecorded. The system platform may takes raw EEG signals from multiplechannels across the scalp and remove unwanted artifacts from the signalsand replace them with de-noised EEG. A Fast Fourier Transform applied toeach EEG signal and theta and beta band power is determined. The ratioof theta/beta power is compared to a ratio and if it exceeds the ratiothen the person is diagnosed as exhibiting ADHD. Enhancements to theinvention include using transformations of the EEG signal to emphasizethe contribution of some EEG channels over others and to consider thecontribution of spatial patterns.

ADHD—Therapy

In another possible implementation of the invention, a computer systemmay be provided for ameliorating attention deficit disorder (“ADD”) orADHD symptomology. For example, a user (adult or child) may see his/herlevel of alpha, beta or theta waves as recorded from a 1, 2, 3 or 4electrode system, while he/she is performing an everyday task. Theinformation can be displayed on a smartphone, tablet, computer or otherdevice. The information can be presented visually in the form of agraph, table, pictorial or rating system. It could also be representedaudibly. In one possible implementation the form of a sound (e.g. pitchor volume) may be changed by the system platform as measurements ofbrain state changes. These changes could also be represented throughvibro-tactile feedback.

This information about his/her brain state could be used to understandthe user's level of ADD or ADHD symptomology, and the user could use thesystem to be encouraged or rewarded to remain in the state of high betawave or high alpha wave or upper alpha, and low theta wave. The systemmay also implement SCP and gamma effects.

Output of real-time measures related to the user's brain-state and orbrain-wave phase may enable applications that can modulate thepresentation of stimulus so that the system platform may eitheremphasize or deemphasize the effect of the stimulus on the user. Forexample, timing the switching of images may be altered by the systemplatform so that a user is more likely to be able to remember theimages.

The system platform could stop advancement of an audio book or mediaplayer when the user is not paying attention. This can also be used tochallenge a user to stay focused on sensory input, such as the attentionpaced audio book, or to give the user a workout when watchingtelevision. The system platform could be configured to change televisionsignal characteristics such as improving contrast, changing presentationof advertising, changing the frequency response of the audio or visualchannel, or perform beamforming to accentuate a particular spatialdomain. Stimulus modulation can be used to help a user snap out oflaxity, or self-narrative, through highlighting sensory input.

The rules engine of the system platform may provide for the systemplatform to determine what settings should be emphasized orde-emphasized on a per-user basis. The user could have a distractionknob, that uses adaptive filtering to be optimize based on usersettings.

Hack Your Own Brain Real-Time Graphing Tools

In another possible implementation of the present invention, graphictools may be provided to aid human assisted pattern recognition of brainstates. This aspect of the invention may involve the presentation of theaccumulation of two dimensional feature space data that is the result ofreal-time feature (spectral or other) extraction on a user's brainwavedata. The user may monitor the user's own brain-state in an openmeditation (mindfulness) style, and simultaneously observe the graphicalrepresentation to find correlations between the user's brain-state orbrain-state dynamics with the two dimensional feature subspacepresented. The graphic tools may include settings for time lagpresentation so that observations of the user's internal state andobservation of the feature space can be serialized. Tunable temporalband-pass filtering of the feature space may be provided, which a usermay configure using settings, in order to aid an experimenter incomparing state dynamics.

Cognitive Training EEG System, Including ADHD Treatment

In another possible implementation, a user wears a soft-band brainwaveheadset that is connected to their computer or mobile phone viaBLUETOOTH™ while the user executes cognitive training exercises that aremodulated by target brain-states. The design may enhance in-domainproblem solving through practice and facilitates crossover throughtarget state association.

For example, in one particular implementation a math problems game maybe provided that utilizes EEG for scoring and difficulty scaling. Forscoring, target brain states may be rewarded using point scaling and a/vembellishment. Problems may be provided where EEG controls difficulty(to make easier when in target brain states) through increasedinformation to aid solution (like making hints visible), through theselection of easier problems and modulating time constrains.

In order to enhance skill crossover, appropriate brain states may belinked to incentives or rewards. Selection of these states may be basedon cognitive training research. These brain states may be incorporatedinto a game environment. Examples of target states may include:sustaining a beta brainwave during problem solving such as math ormemory, seek and find, and matching tasks; and sustaining alphabrainwaves while performing mental rotation. In all tasks betabrainwaves may result in a change in the level of difficulty of thegame, affect audio or affect displayed content on a display of theclient computing device.

A Text Editor that Varies Font (Size, Shape, Colour) with Mood

A further example of an implementation of the present invention includesa text editor that automatically adjusts the font used when a user istyping, based on the user's brain state.

In-Flight Entertainment System

In another possible implementation of the present invention, anin-flight entertainment system may be provided that combines brainwaveinput, and audio/visual output on an airplane. Biofeedback may beprovided to attempt to make travelling by air more pleasurable topassengers. The system may be configured to help a user of the system torelax, and control anxiety.

General

In accordance with aspects of the present invention, there is provided asystem comprising: at least one client computing device; at least onebio-signal sensor at and in communication with the at least one clientcomputing device; and at least one computer server in communication withthe at least one computing device over a communications network; the atleast one client computing device configured to: receive time-codedbio-signal data of a user from the at least one bio-signal sensor;transmit the time-coded bio-signal data and acquired time-coded featureevent data to the at least one computer server; receive from the atleast one computer server a user-response classification of at leastpart of the time-coded bio-signal data based on an identified pattern ofcorrespondence between the at least part of the time-coded bio-signaldata and at least part of the time-coded feature event data at at leastone respective time code; in accordance with a received input confirmingthe user-response classification, update a bio-signal interactionprofile at the at least one client computing device with theuser-response classification, the at least part of the time-codedbio-signal data, and the at least part of the time-coded feature eventdata at the at least one respective time code. By classifying the user'sbio-signal data with a particular user-response classification, thesystem may provide for more accurate, or more responsive interpretationof user bio-signal data received at the at least one client computingdevice.

The at least one client computing device may be further configured totransmit a confirmation of the user-response classification to the atleast one computer server. For example, the user may be asked to confirmthe classification of the user's bio-signal data as a particularuser-response. The at least one computer server may be configured toupdate a bio-signal interaction profile associated with the user at theat least one computer server with the user-response classification, theat least part of the time-coded bio-signal data, and the at least partof the time-coded feature event data at the at least one respective timecode, based on the confirmation received from the user. The bio-signalinteraction profile associated with the user may be anonymized such thatany information stored in the profile used by the system platform toanalyze bio-signal data of another user could not be used to identify aparticular user.

The at least one computer server may be configured to identify acorrespondence between the at least part of the time-coded feature eventdata stored in the bio-signal interaction profile stored at the at leastone computer server with time-coded feature event data received from atleast one second client computing device associated with a second user.The system may be configured to identify and determine where multipleusers have experienced the same feature events. Where this has occurred,the at least one computer server may be configured to classify at leastpart of time-coded bio-signal data received from the at least one secondclient computing device corresponding to the time-coded feature eventdata received from the at least one second client computing inaccordance with the user-response classification. Accordingly, thesystem platform may attempt to classify the second user's bio-signaldata based on a user-response classification determined by the systemplatform for the prior user. The at least one computer server may beconfigured to then update a bio-signal interaction profile associatedwith the second user with the classification of the at least part of thetime-coded bio-signal data received from the at least one second clientcomputing device, the at least part of the time-coded bio-signal datareceived from the at least one second client computing device, and theat least part of the time-coded feature event data received from the atleast one second client computing device, in accordance with a receivedconfirmation of the user-response classification from the at least onesecond client computing device. An application where this may be usefulis in comparing bio-signal responses of one user to another user whenexperiencing playback of media, such as music or video. The acquiredtime-coded feature event data may therefore comprise an identificationof playback of a particular media, and the user-response classificationmay comprise a determination of the user having liked the particularmedia. The at least one computer server may be configured to transmit arecommendation of at least one other particular media to the at leastone second client computing device whose user-response classificationcorresponded to the prior user's user-response classification.

The system platform may include at least one non-bio-signal sensor atand in communication with the at least one client computing device, theat least one client computing device configured to: receive time-codednon-bio-signal sensor data from the at least one non-bio-signal sensor;wherein the acquired time-coded feature event data is based at leastpartly on the received time-coded non-bio-signal sensor data.

The acquired time-coded feature event data may be based at least partlyon calendar data stored on the at least one client computing device.

The acquired time-coded feature event data may be based at least partlyon event data input from the user.

The at least one bio-signal sensor may comprise a head-mountablebrainwave sensor, and the time-coded bio-signal sensor data comprisesbrainwave data of the user.

The at least one client computing device may be configured to transmitat least part of the bio-signal sensor data to at least one secondclient computing device for use as input by the at least one secondclient computing device.

The at least one client computing device may be configured to transmitthe user-response classification to at least one second client computingdevice for use as input by the at least one second client computingdevice.

The at least one computer server may be configured to transmit at leastpart of the bio-signal sensor data to at least one second clientcomputing device for use as input by the at least one second clientcomputing device.

The at least one computer server may be configured to transmit theuser-response classification to at least one second client computingdevice for use as input by the at least one second client computingdevice.

The at least one client computing device may be configured to updatenotification settings of the at least one client computing device inaccordance with an identification of subsequently-received time-codedbio-signal data corresponding to the user-response classification.

The computer server may be configured to determine the user-responseclassification based at least partly on a type of the at least onebio-signal sensor.

The at least one client computing device may be configured to process atleast part of the time-coded bio-signal data in accordance with thebio-signal interaction profile at the client computing device, andtransmit the processed time-coded bio-signal data to the at least onecomputer server, wherein the user-response classification is based atleast partly on the processed time-coded bio-signal data.

In accordance with an aspect of the present invention, there may beprovided a computer network implemented system for improving theoperation of one or more biofeedback computer systems is providedcomprising: one or more server computers linked to the Internet, andenabling one or more computer implemented utilities providing: a braincomputer interface for connecting to one or more applications forenabling one or more biofeedback computer systems, the brain computerinterface linking to one or more sensors that include at least one brainwave sensor and optionally a non-bio-signal data sensor; an intelligentbio-signal processing system that is operable to: capture bio-signaldata and in addition optionally non-bio-signal data; and analyze thebio-signal data and non-bio-signal data, if any, so as to: extract oneor more features related to at least one individual interacting with thebiofeedback computer system; classify the individual based on thefeatures by establishing one or more brain wave interaction profiles forthe individual for improving the interaction of the individual with theone or more biofeedback computer systems, and initiate the storage ofthe brain waive interaction profiles to a database; and access one ormore machine learning components or processes for further improving theinteraction of the individual with the one or more biofeedback computersystems by updating automatically the brain wave interaction profilesbased on detecting one or more defined interactions between theindividual and the one or more of the biofeedback computer systems.

It will be appreciated that any module or component exemplified hereinthat executes instructions may include or otherwise have access tocomputer readable media such as storage media, computer storage media,or data storage devices (removable and/or non-removable) such as, forexample, magnetic disks, optical disks, tape, and other forms ofcomputer readable media. Computer storage media may include volatile andnon-volatile, removable and non-removable media implemented in anymethod or technology for storage of information, such as computerreadable instructions, data structures, program modules, or other data.Examples of computer storage media include RAM, ROM, EEPROM, flashmemory or other memory technology, CD-ROM, digital versatile disks(DVD), blue-ray disks, or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by an application, module, or both. Any suchcomputer storage media may be part of the mobile device, trackingmodule, object tracking application, etc., or accessible or connectablethereto. Any application or module herein described may be implementedusing computer readable/executable instructions that may be stored orotherwise held by such computer readable media.

Thus, alterations, modifications and variations can be effected to theparticular embodiments by those of skill in the art without departingfrom the scope of this disclosure, which is defined solely by the claimsappended hereto.

The present system and method may be practiced in various embodiments. Asuitably configured computer device, and associated communicationsnetworks, devices, software and firmware may provide a platform forenabling one or more embodiments as described above. By way of example,FIG. 17 shows a generic computer device 500 that may include a centralprocessing unit (“CPU”) 502 connected to a storage unit 504 and to arandom access memory 506. The CPU 502 may process an operating system501, application program 503, and data 523. The operating system 501,application program 503, and data 523 may be stored in storage unit 504and loaded into memory 506, as may be required. Computer device 500 mayfurther include a graphics processing unit (GPU) 522 which isoperatively connected to CPU 502 and to memory 506 to offload intensiveimage processing calculations from CPU 502 and run these calculations inparallel with CPU 502. An operator 507 may interact with the computerdevice 500 using a video display 508 connected by a video interface 505,and various input/output devices such as a keyboard 510, mouse 512, anddisk drive or solid state drive 514 connected by an I/O interface 509.In known manner, the mouse 512 may be configured to control movement ofa cursor in the video display 508, and to operate various graphical userinterface (GUI) controls appearing in the video display 508 with a mousebutton. The disk drive or solid state drive 514 may be configured toaccept computer readable media 516. The computer device 500 may formpart of a network via a network interface 511, allowing the computerdevice 500 to communicate with other suitably configured data processingsystems (not shown).

In further aspects, the disclosure provides systems, devices, methods,and computer programming products, including non-transientmachine-readable instruction sets, for use in implementing such methodsand enabling the functionality described previously.

Although the disclosure has been described and illustrated in exemplaryforms with a certain degree of particularity, it is noted that thedescription and illustrations have been made by way of example only.Numerous changes in the details of construction and combination andarrangement of parts and steps may be made. Accordingly, such changesare intended to be included in the invention, the scope of which isdefined by the claims.

Except to the extent explicitly stated or inherent within the processesdescribed, including any optional steps or components thereof, norequired order, sequence, or combination is intended or implied. As willbe will be understood by those skilled in the relevant arts, withrespect to both processes and any systems, devices, etc., describedherein, a wide range of variations is possible, and even advantageous,in various circumstances, without departing from the scope of theinvention, which is to be limited only by the claims.

What is claimed is:
 1. A system comprising: at least one clientcomputing device; at least one bio-signal sensor at and in communicationwith the at least one client computing device; and at least one computerserver in communication with the at least one computing device over acommunications network; the at least one client computing deviceconfigured to: receive time-coded bio-signal data of a user from the atleast one bio-signal sensor; transmit the time-coded bio-signal data andacquired time-coded feature event data to the at least one computerserver; receive from the at least one computer server a user-responseclassification of at least part of the time-coded bio-signal data basedon an identified pattern of correspondence between the at least part ofthe time-coded bio-signal data and at least part of the time-codedfeature event data at at least one respective time code; in accordancewith a received input confirming the user-response classification,update a bio-signal interaction profile at the at least one clientcomputing device with the user-response classification, the at leastpart of the time-coded bio-signal data, and the at least part of thetime-coded feature event data at the at least one respective time code.2. The system of claim 1 wherein the at least one client computingdevice is further configured to transmit a confirmation of theuser-response classification to the at least one computer server.
 3. Thesystem of claim 2 wherein the at least one computer server is configuredto update a bio-signal interaction profile associated with the user atthe at least one computer server with the user-response classification,the at least part of the time-coded bio-signal data, and the at leastpart of the time-coded feature event data at the at least one respectivetime code.
 4. The system of claim 3 wherein the bio-signal interactionprofile associated with the user is anonymized.
 5. The system of claim 3wherein the at least one computer server is configured to identify acorrespondence between the at least part of the time-coded feature eventdata stored in the bio-signal interaction profile stored at the at leastone computer server with time-coded feature event data received from atleast one second client computing device associated with a second user.6. The system of claim 5 wherein the at least one computer server isconfigured to classify at least part of time-coded bio-signal datareceived from the at least one second client computing devicecorresponding to the time-coded feature event data received from the atleast one second client computing in accordance with the user-responseclassification.
 7. The system of claim 6 wherein the at least onecomputer server is configured to update a bio-signal interaction profileassociated with the second user with the classification of the at leastpart of the time-coded bio-signal data received from the at least onesecond client computing device, the at least part of the time-codedbio-signal data received from the at least one second client computingdevice, and the at least part of the time-coded feature event datareceived from the at least one second client computing device, inaccordance with a received confirmation of the user-responseclassification from the at least one second client computing device. 8.The system of claim 7 wherein the acquired time-coded feature event datacomprises an identification of playback of a particular media, theuser-response classification comprises a determination of the userhaving liked the particular media; the at least one computer serverconfigured to transmit a recommendation of at least one other particularmedia to the at least one second client computing device.
 9. The systemof claim 1 further comprising at least one non-bio-signal sensor at andin communication with the at least one client computing device, the atleast one client computing device configured to: receive time-codednon-bio-signal sensor data from the at least one non-bio-signal sensor;wherein the acquired time-coded feature event data is based at leastpartly on the received time-coded non-bio-signal sensor data.
 10. Thesystem of claim 1 wherein the acquired time-coded feature event data isbased at least partly on calendar data stored on the at least one clientcomputing device.
 11. The system of claim 1 wherein the acquiredtime-coded feature event data is based at least partly on event datainput from the user.
 12. The system of claim 1 wherein the at least onebio-signal sensor comprises a head-mountable brainwave sensor, and thetime-coded bio-signal sensor data comprises brainwave data of the user.13. The system of claim 1 wherein the at least one client computingdevice is configured to transmit at least part of the bio-signal sensordata to at least one second client computing device for use as input bythe at least one second client computing device.
 14. The system of claim1 wherein the at least one client computing device is configured totransmit the user-response classification to at least one second clientcomputing device for use as input by the at least one second clientcomputing device.
 15. The system of claim 4 wherein the at least onecomputer server is configured to transmit at least part of thebio-signal sensor data to at least one second client computing devicefor use as input by the at least one second client computing device. 16.The system of claim 4 wherein the at least one computer server isconfigured to transmit the user-response classification to at least onesecond client computing device for use as input by the at least onesecond client computing device.
 17. The system of claim 1 wherein the atleast one client computing device is configured to update notificationsettings of the at least one client computing device in accordance withan identification of subsequently-received time-coded bio-signal datacorresponding to the user-response classification.
 18. The system ofclaim 1 wherein the computer server is configured to determine theuser-response classification based at least partly on a type of the atleast one bio-signal sensor.
 19. The system of claim 1 comprising the atleast one client computing device configured to process at least part ofthe time-coded bio-signal data in accordance with the bio-signalinteraction profile at the client computing device, and transmit theprocessed time-coded bio-signal data to the at least one computerserver, wherein the user-response classification is based at leastpartly on the processed time-coded bio-signal data.
 20. A method,performed by at least one client computing device in communication withat least one bio-signal sensor at the at least one client computingdevice, the at least one client computing device in communication withat least one computer server over a communications network, the methodcomprising: receiving time-coded bio-signal data of a user from the atleast one bio-signal sensor; transmitting the time-coded bio-signal dataand acquired time-coded feature event data to the at least one computerserver; receiving from the at least one computer server a user-responseclassification of at least part of the time-coded bio-signal data basedon an identified pattern of correspondence between the at least part ofthe time-coded bio-signal data and at least part of the time-codedfeature event data at at least one respective time code; in accordancewith a received input confirming the user-response classification,updating a bio-signal interaction profile at the at least one clientcomputing device with the user-response classification, the at leastpart of the time-coded bio-signal data, and the at least part of thetime-coded feature event data at the at least one respective time code.21. The method of claim 20 comprising transmitting a confirmation of theuser-response classification to the at least one computer server. 22.The method of claim 21 wherein the at least one computer server isconfigured to update a bio-signal interaction profile associated withthe user at the at least one computer server with the user-responseclassification, the at least part of the time-coded bio-signal data, andthe at least part of the time-coded feature event data at the at leastone respective time code.
 23. The method of claim 22 wherein thebio-signal interaction profile associated with the user is anonymized.24. The method of claim 22 wherein the at least one computer server isconfigured to identify a correspondence between the at least part of thetime-coded feature event data stored in the bio-signal interactionprofile stored at the at least one computer server with time-codedfeature event data received from at least one second client computingdevice associated with a second user.
 25. The method of claim 24 whereinthe at least one computer server is configured to classify at least partof time-coded bio-signal data received from the at least one secondclient computing device corresponding to the time-coded feature eventdata received from the at least one second client computing inaccordance with the user-response classification.
 26. The method ofclaim 25 wherein the at least one computer server is configured toupdate a bio-signal interaction profile associated with the second userwith the classification of the at least part of the time-codedbio-signal data received from the at least one second client computingdevice, the at least part of the time-coded bio-signal data receivedfrom the at least one second client computing device, and the at leastpart of the time-coded feature event data received from the at least onesecond client computing device, in accordance with a receivedconfirmation of the user-response classification from the at least onesecond client computing device.
 27. The method of claim 26 wherein theacquired time-coded feature event data comprises an identification ofplayback of a particular media, the user-response classificationcomprises a determination of the user having liked the particular media;the at least one computer server configured to transmit a recommendationof at least one other particular media to the at least one second clientcomputing device.
 28. The method of claim 20 wherein at least onenon-bio-signal sensor is located at and is in communication with the atleast one client computing device, the method comprising: receivingtime-coded non-bio-signal sensor data from the at least onenon-bio-signal sensor; wherein the acquired time-coded feature eventdata is based at least partly on the received time-coded non-bio-signalsensor data.
 29. The method of claim 20 wherein the acquired time-codedfeature event data is based at least partly on calendar data stored onthe at least one client computing device.
 30. The method of claim 20wherein the acquired time-coded feature event data is based at leastpartly on event data input from the user.
 31. The method of claim 20wherein the at least one bio-signal sensor comprises a head-mountablebrainwave sensor, and the time-coded bio-signal sensor data comprisesbrainwave data of the user.
 32. The method of claim 20 comprisingtransmitting at least part of the bio-signal sensor data to at least onesecond client computing device for use as input by the at least onesecond client computing device.
 33. The method of claim 20 comprisingtransmitting the user-response classification to at least one secondclient computing device for use as input by the at least one secondclient computing device.
 34. The method of claim 23 wherein the at leastone computer server is configured to transmit at least part of thebio-signal sensor data to at least one second client computing devicefor use as input by the at least one second client computing device. 35.The method of claim 23 wherein the at least one computer server isconfigured to transmit the user-response classification to at least onesecond client computing device for use as input by the at least onesecond client computing device.
 36. The method of claim 20 comprisingupdating notification settings of the at least one client computingdevice in accordance with an identification of subsequently-receivedtime-coded bio-signal data corresponding to the user-responseclassification.
 37. The method of claim 20 wherein the computer serveris configured to determine the user-response classification based atleast partly on a type of the at least one bio-signal sensor.
 38. Themethod of claim 20 comprising processing at least part of the time-codedbio-signal data in accordance with the bio-signal interaction profile atthe client computing device, and transmitting the processed time-codedbio-signal data to the at least one computer server, wherein theuser-response classification is based at least partly on the processedtime-coded bio-signal data.
 39. A computer network implemented systemfor improving the operation of one or more biofeedback computer systemsis provided comprising: (a) one or more server computers linked to theInternet, and enabling one or more computer implemented utilitiesproviding: (i) a brain computer interface for connecting to one or moreapplications for enabling one or more biofeedback computer systems, thebrain computer interface linking to one or more sensors that include atleast one brain wave sensor and optionally a non-bio-signal data sensor;(ii) an intelligent bio-signal processing system that is operable to:(A) capture bio-signal data and in addition optionally non-bio-signaldata; and (B) analyze the bio-signal data and non-bio-signal data, ifany, so as to: (I) extract one or more features related to at least oneindividual interacting with the biofeedback computer system; (II)classify the individual based on the features by establishing one ormore brain wave interaction profiles for the individual for improvingthe interaction of the individual with the one or more biofeedbackcomputer systems, and initiate the storage of the brain waiveinteraction profiles to a database; and (III) access one or more machinelearning components or processes for further improving the interactionof the individual with the one or more biofeedback computer systems byupdating automatically the brain wave interaction profiles based ondetecting one or more defined interactions between the individual andthe one or more of the biofeedback computer systems.